Carbon storage through China’s planted forest expansion

Domain: earth_science
Published: 2024-05-15
Editorial outcome: minor_revision

Abstract
China’s extensive planted forests play a crucial role in carbon storage, vital for climate change mitigation. However, the complex spatiotemporal dynamics of China’s planted forest area and its carbon storage remain uncaptured. Here we reveal such changes in China’s planted forests from 1990 to 2020 using satellite and field data. Results show a doubling of planted forest area, a trend that intensified post-2000. These changes lead to China’s planted forest carbon storage increasing from 675.6±12.5 Tg C in 1990 to 1,873.1±16.2 Tg C in 2020, with an average rate of ~40 Tg C yr−1. The area expansion of planted forests contributed ~53% (637.2±5.4 Tg C) of the total above increased carbon storage in planted forests compared with planted forest growth. This proactive policy-driven expansion of planted forests has catalyzed a swift increase in carbon storage, aligning with China’s Carbon Neutrality Target for 2060.

Introduction
Forests function as important carbon (C) sinks1–6 and offer a natural solution to address climate change and ecological issues7–9. The expansion of planted forest areas is considered an effective solution to achieve the main objectives of the UN’s Global Forest Goals under continuous decrease in natural forest area10. However, the decrease in natural forest area outweighs the increase in planted forest area, resulting in a global net decline of forest area by 178 million ha and a reduction in the global forest C stock from 668 Petagram (Pg) to 662 Pg between 1990 and 202011. Achieving the goals of the 2030 Agenda12 relies on credible monitoring and verification of the C storage of planted forests. This task needs to take into account land spatiotemporal dynamics and forest types, distinguishing between planted and natural forests due to different species composition, stand structure, age, and management context13–16. In China, which has the world’s largest planted forest area, accounting for over 1/4 of global planted forest area17, a comprehensive analysis of its planted forest C storage needs investigation.

China’s achievement in expanding planted forest area is a remarkable accomplishment both domestically and in the global context18. Despite this global reduction of forest area, China’s planted forest area expansion has made significant contributions to increasing China’s forest area for several consecutive years19,20. As of 2020, China’s forest area reached approximately 220 million ha, accounting for 5% of the world’s forest area11. This expansion is a result of conducted government efforts to convert croplands, shrublands, and grasslands into planted forests17,18,21,22. This effort to grow the planted forest area not only increased China’s total forest extent but also resulted in drastic changes in land use and land cover (LULC), impacting China’s forests C storage capacity5,11,23–26. Based on the National Forest Management Plan (2016–2050), China’s forest area will continue to account for ~26% of China’s total land area by 2050, requiring an additional conversion of over 2.8 million ha from other LULC to forests20. This has highlighted the urgent need for accurately quantifying the spatiotemporal dynamics of planted forest conversion from other LULC types13,27 and devising an optimal C storage strategy to take advantage of the planted forest expansion28,29.

However, currently available maps of China’s planted forests have limitations in both spatial and temporal coverage30,31. These existing national maps and datasets were created through forest inventories or the digitization of forest inventory maps for specific years with coarse spatial resolutions21,32 or only pertain to specific subtypes23,33,34, and are thus inadequate for tracking China’s multi-decadal efforts in the planted forest area expansion13,35,36. Consequently, there is an urgent need to conduct high spatial resolution, national-scale research, and long-time-series assessments of C storage associated with the planted forest area expansion in China.

For this purpose, leveraging all available Landsat-4/5/7/8/9 surface reflectance images from 1990 to 2020 in the Google Earth Engine (GEE) cloud computing platform together with field samples, we generated the high-resolution wall-to-wall planted forest maps for China. These efforts yielded the ability to capture the dynamics of planted forest coverage at five-year intervals from 1990 to 2020 at 30-m spatial resolution (see Methods and Supplementary Information). Integrating the data on China’s planted forests’ spatiotemporal dynamics, LULC conversions, vegetation C densities, and vegetation type maps, we then analyzed China’s planted forest C storage from 1990 to 2020. The resulting comprehensive dataset not only contributes to the understanding of the impact of China’s planted forest expansion but also serves as a valuable tool for evaluating its implications on climate change and sustainable development within the country.

Results
Mapping planted forest spatiotemporal dynamics in China
Quinquennial maps of China’s planted forest from 1990 to 2020 reveal that the area of planted forests expanded at an annual rate of 14,613 km2, representing a 94.33% net increase from 464,715 km2 in 1990 to 903,099 km2 by 2020 (Fig. 1a, b, e). This increase in planted forest area was the combined result of a gain of 479,681 km2 and a loss of 41,297 km2, leading to a net increase of 438,384 km2 (Fig. 1e and Supplementary Fig. 1). Our maps were compared with planted forest map of National Forest Inventory and the reported National Forestry Statistical Yearbook, showing minor differences in the area and significantly positive relationships (R2 ranged from 0.8 to 0.9, P < 0.01) (Supplementary Figs. 2 and 3). The overall accuracies for the generated quinquennial maps ranged from 77.3% to 81.8% from 1990 to 2020 (Supplementary Fig. 4). These accuracy assessments support the reliability of the generated maps of China’s planted forests, providing confidence that our maps align with existing datasets and fulfill the aims of this study.

China’s planted forests adhere to typical forest distribution patterns in terms of latitudinal, longitudinal, and elevational distribution32. In 2020, planted forests were predominantly concentrated in regions with elevations below 1,500 m (Fig. 1c). The majority of planted forests were located in the southern region, accounting for 32.6% of the total area of planted forests, followed by the eastern region (20.1%) and the southwestern region (16.6%) (Fig. 1a, b). The northwestern region, on the other hand, had the smallest proportion of planted forest area, covering only 5.5% of its total area (Fig. 1 and Supplementary Fig. 5), but had the fastest rate of planted forest area expansion, growing by 266.5% from 1990 to 2020 (Fig. 1f). Overall, all regions showed a net increase, albeit with significant fluctuations around 2005 (Fig. 1f and Supplementary Fig. 6b). The south experienced the highest increase rate, with a total increase of 150,088 km2 and an average increase rate of 5,003 km2 per year (Fig. 1f). The southwest (103,729 km2) and the east (73,208 km2) also exhibited substantial expansion (Fig. 1f). By aggregating the 30-meter resolution planted forest map into 0.1° grid cells, we analyzed the characteristics of planted forest area changes at the grid scale. We found that 91% of the changed pixels from 1990 to 2020 showed positive trends, with 84.0% of them being statistically significant (P < 0.01) (Supplementary Fig. 7a). These positive trends were mostly observed in the east and south (Supplementary Fig. 7a). In terms of the changing trends in the expansion rate of planted forest areas, we observed that only 7.4% of the changed pixels exhibited positive trends, with 24.4% being statistically significant (P < 0.01) (Supplementary Fig. 7b), which were mainly distributed in the southwestern, northwestern, and northern regions (Supplementary Fig. 7b). In most regions of China, the rate of planted forest area expansion showed a decline, which is consistent with the trend observed in China’s annual afforestation and reforestation area from 1990 to 2020, as reported in the National Forestry Statistical Yearbook (Supplementary Fig. 8), particularly in the eastern, southern, and northern regions29, where the suitable area for plantation is nearing saturation (Supplementary Fig. 8).

Changes in planted forest C storage
The dynamics of China’s planted forest C storage in biomass from 1990 to 2020 exhibited marked spatiotemporal variations (Fig. 2). Overall, we found a trend of increasing C storage in China’s planted forest, increasing from 675.6±12.5 (mean±standard deviation) Teragram (Tg) C in 1990 to 1,873.1±16.2 Tg C in 2020, with an annual increment of approximately 40.0 Tg C (Fig. 3a and Supplementary Table 1). The increase in C storage of China’s planted forests closely aligned with the spatiotemporal dynamics of planted forest distribution and expansion (Figs. 1d and 2c). Since 1990, the most substantial increases in C storage within China’s planted forests have been observed in regions with extensive forest cover (Figs. 1 and 2). The increased C storage was primarily attributed to the ongoing expansion of the planted forest area. However, the growth of young planted forests also significantly contributed to the increase of C storage (Fig. 3). Despite observed regional declines (Fig. 2c), the overarching trend of increasing C storage remains unabated (Figs. 2 and 3).

The C storage in planted forests in southern, eastern, and southwestern China increased the most, with respective gains of 386.4±20.6 Tg C, 272.6±18.1 Tg C, and 237.9±15.8 Tg C from 1990 to 2020 (Fig. 3e–g). Despite an initial decline in C storage in the northeastern region between 1990 to 1995, there was an overall increase of 103.1±20.4 Tg C by 2020 (Figs. 2c and 3d). The historically forest-scarce northern and northwestern (Fig. 1) regions exhibited increasing trends in C storage of planted forests, attributable to the expansion of planted forests, with increases from 43.3±4.2 Tg C to 147.8±5.0 Tg C and from 29.2±2.6 Tg C to 122.2±4.2 Tg C in 1990 and 2020, respectively. Overall, the rate of increase in C storage in planted forests before 2005 was comparatively modest compared to the period following 2005 (Fig. 3a), averaging approximately 20 Tg C/a between 1990 and 2005 (Fig. 3a and Supplementary Table 1). However, the C storage rates in planted forests across different regions experienced a marked acceleration after 2005 (Fig. 3b–g), with an average storage rate of almost 60 Tg C/a from 2005 to 2020 and peaking at a sequestration rate of approximately 73 Tg C/a between 2005 and 2010 (Fig. 3a and Supplementary Table 1).

Attributions of LULC transformation on planted forest C storage
The spatiotemporal analysis revealed a synchronization between the expansion of planted forests from LULC conversion and the associated increase in planted forest C storage (Fig. 4). An analysis of LULC transformation between 1990 and 2020 indicated that 438,787 km2 of planted forests originated from the conversion of croplands, shrublands, grasslands, and natural forests, representing 98.3% of the overall increase in planted forest area (Fig. 4a). During the same period, the conversion of different LULC to planted forests resulted in 637.2±5.4 Tg C, approximately 53.2% of the overall increase of planted forest C storage (Supplementary Table 2). Nonetheless, the contribution from LULC conversion to the C storage of planted forests varied across different periods, with the highest C storage contribution occurring from 1990 to 1995, reaching 81.4%, and declining to a mere 17.2% from 2000 to 2005 (Supplementary Table 2). This variability can be attributed to the expansion rate of China’s planted forests in distinct periods, along with the spatiotemporal variations in the types of LULC converted into planted forests (Fig. 4b, c). Until 2000, expansions were mainly from natural forests, shrublands, and croplands (Fig. 4a, c). Post-2000, conversions from cropland, shrublands, and grasslands emerged as the principal contributors (Fig. 4a, c). The classification of conversion events indicated that over one-third of single-change events involved transitions from croplands to planted forests, succeeded by conversions from shrublands, grasslands, and natural forests (see Methods and Supplementary Figs. 9 and 10). Multi-change events, accounting for 7.2% of all transitions, predominantly occurred in the southern and southwestern regions (Supplementary Figs. 9 and 10). The prevalent cropland-to-planted forest conversions in both single-change and multi-change events (Supplementary Figs. 9 and 10) highlight the impact of the Grain for Green (GFG) program initiated in 1999. Consequently, between 1990 and 2020, C storage gains from cropland-to-planted forest conversions amounted to 191.7±2.6 Tg C, with shrubland and grassland conversions contributing 176.4±2.3 Tg C and 135.9±2.0 Tg C to planted forest C storage, respectively (Supplementary Table 2). C storage gains from the conversion of natural forests to planted forests were relatively lower, with an estimated 121.7±3.5 Tg C, constituting about 19.1% of the total increase in C storage from LULC conversion (Supplementary Table 2).

We conducted a more detailed analysis of C storage changes due to LULC conversion into planted forests across various regions in China, revealing substantial regional disparities in the contribution of these conversions to augmenting C storage in planted forests (Fig. 5). In both the northwestern and northern regions, grassland conversion emerged as the predominant factor driving the increase in C storage within planted forests (Fig. 5). In the northwestern region, 34.5% of the C storage increase in planted forests from 1990 to 2020 was contributed by the conversion of grassland, while in the northern region, the contribution of grassland was 25.9% (Fig. 5 and Supplementary Table 3). In the northeastern region, conversions from cropland and natural forests to planted forests significantly contributed to the region’s planted forest C storage increase, accounting for 27.4% and 19.1% of the total increase, respectively (Fig. 5 and Supplementary Table 3). The eastern region witnessed a notable increase in C storage in its planted forests, with cropland and natural forest conversions contributing 11.9% and 13.0% to the overall increase, respectively (Fig. 5 and Supplementary Table 3). In southern and southwestern China, the main conversions were from cropland and shrubland, especially in the southwestern region, where the conversion of shrubland contributed 25.3% to the increase in C storage of planted forests in this region, and might be the main reason for the sharp increase in C storage of planted forests from 2010 to 2015 (Fig. 5 and Supplementary Table 3).

Methods
Definitions of natural and planted forests in this study
In this study, forests are defined as areas with tree height exceeding 5 meters and canopy cover exceeding 15% within a 30-meter resolution pixel, planted forests refer to a forest ecosystem established by artificial tree planting or seeding for the provision of income and goods, as well as for climate change mitigation and the restoration of ecosystem services and processes17,50. Natural forests refer to a forest that regenerates naturally, with trees growing from naturally dispersed seeds or seedlings, and is typically composed of indigenous tree species and genotypes11,51. In this study, we only focused on the spatiotemporal dynamic of China’s planted forest and resultant C storage in aboveground biomass from 1990 to 2020.

Landsat data
We utilized all available Landsat TM/ETM/OLI/OLI-2 surface reflectance images for China from January 1, 1988, to December 31, 2021, leveraging the GEE cloud-processing platform. A total of 447,730 atmospherically corrected Landsat scenes, with a cloud cover of less than 30%, were selected from Landsat-4 (1,186 scenes), Landsat-5 (160,830 scenes), Landsat-7 (192,645 scenes), Landsat-8 (91,468 scenes), and Landsat-9 (1,601 scenes). These scenes were used to generate wall-to-wall planted forest maps at five-year intervals from 1990 to 2020 (Supplementary Table 4). At each timestep, we created a stack of images for the target year ± two years (e.g., images from 1988 to 1992 were used for compositing the 1990 mosaic) to minimize missing data and mitigate cloud interference52. Finally, we applied the C function of mask (CFMASK) algorithm, implemented in the GEE platform, to every stack to mask out clouds, cloud shadows, and snow53.

Field samples
The annotated samples of planted forests at various periods formed the basis for generating time-series maps of planted forests in this research. Initially, this research acquired more than 600,000 vegetation survey samples through crowdsourcing, field surveys, and national forest inventories. These samples contain information such as geographic coordinates and vegetation types36, covering the period from 2003 to 2022. With the aid of these samples, our research employed a time-series change detection method to establish a dataset for the recognition of planted forests at different time intervals by evaluating if each sample experienced changes during different time periods. First, based on the vegetation type information of each sample, we selected samples of planted and natural forests. Second, we employed the spatial analysis tool in ArcGIS Pro 3.0 to analyze the forest mask data from the annual China Land Cover Dataset (CLCD)54 from 1990 to 2020 (Supplementary Note 1), resulting in the identification of forest regions that remained unchanged during this period. Third, using the identified unchanged forest regions, we applied spatial filtering to all planted and natural forest samples, preserving only the samples that intersected with the unchanged forest areas, thereby obtaining an initial set of field samples suitable for different time intervals. Fourth, we used the Normalized Difference Vegetation Index (NDVI) time series from Landsat to determine if the minimum NDVI value for each sample in the created dataset from 1990 to 2020 was equal to or greater than 0.655, retaining samples that satisfied this condition. By following these procedures, we acquired a dataset containing 124,407 samples of planted and natural forests, with 70% allocated for model training and 30% for validation (Supplementary Fig. 11).

Mapping planted forests and validation
Significant differences exist in textural and temporal features between natural and planted forests. Previous research has demonstrated that planted forest mapping can be achieved by utilizing these differences in conjunction with a large number of samples at the national scale36. Therefore, we first derived five widely used vegetation indices from Landsat-based surface reflectance data, including the NDVI, enhanced vegetation index (EVI), bare soil index (BSI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI). These indices were appended to the cloud-free image stacks, along with the spectral bands (blue, green, red, near-infrared, shortwave infrared-1, and shortwave infrared-2), to facilitate planted forest extraction (Supplementary Note 2). Second, we computed the median value of each pixel to composite the final image (11 bands). According to the final composite image, textural information was calculated through the gray-level co-occurrence matrix method in GEE, which generated 18 features for each band. The temporal features were calculated by analyzing Landsat NDVI and EVI time series from 1990 to 2020 through harmonic analysis56. Specifically, the magnitude, phase, amplitude, and root mean square error (RMSE) of the fitted NDVI and EVI time series were extracted to constitute a temporal feature set56,57 (Supplementary Note 2). Additionally, topographic information (elevation, slope, and aspect) was retrieved through digital elevation model (DEM) data from the Shuttle Radar Topography Mission (Supplementary Note 2). In summary, 220 features were used for mapping planted forests, including 11 spectral bands, 198 textural features, 8 temporal features, and 3 topographic features (Supplementary Data 1).

To reduce the number of features used for planted forest mapping, we applied the recursive feature elimination cross-validation (RFE-CV) feature selection method at the regional scale (Supplementary Note 3) and established the optimal feature collection for the northeastern, northern, northwestern, southern, and southwestern regions at different periods (Supplementary Fig. 12). Third, a classification model adapted to each region (Supplementary Data 1) and period was constructed according to a random forest classifier and used to identify planted forests, respectively. Finally, we generated planted forest maps for the years 1990, 1995, 2000, 2005, 2010, 2015, and 2020 (Supplementary Fig. 1).

The resultant time-series planted forest maps were validated through four data sources (Supplementary Note 4). First, we used 30% of the independent field samples to evaluate the maps (Supplementary Note 4 and Supplementary Fig. 11). Second, a systematic random sampling was implemented to verify the generated planted forest maps (Supplementary Note 4 and Supplementary Fig. 13). We also collected statistical data on planted forest area at national and provincial scales for the period 2000-2020 and compared resultant maps with the statistical data (Supplementary Note 4). Furthermore, the digitized planted forest map based on the Seventh National Forest Resource Inventory conducted from 2004 to 200821 was further obtained and used to compare the mapped results for the years 2000, 2005, and 2010 (Supplementary Note 4). The validation results indicated that the generated planted forest maps had a comparable accuracy and exhibited a high degree of concordance with existing maps and statistical data at national and provincial scales (Supplementary Figs. 2, 3, and 4).

Planted forest spatiotemporal dynamic analysis
To gain a comprehensive understanding of the spatiotemporal dynamics of planted forests in China, this research initially aggregated the planted forest areas for each period into a 0.1° grid and subsequently analyzed the changes of planted forest area, the area added every five years, and their trends at national, regional and grid scales using simple linear regression with a t-test at the 5% significance level based on the “lm” function in the R software.

Furthermore, we employed the image change detection tool in ArcGIS Pro 3.0, based on the CLCD land cover data corresponding to 1990, 1995, 2000, 2005, 2010, 2015, and 2020, to analyze land cover changes resulting from the expansion of planted forest area. The focus was to identify the land cover types (e.g., cropland, grassland, etc.) that underwent conversion to planted forests in each period.

Lastly, we conducted a statistical analysis of the change frequency for each pixel within the changing areas, dividing them into single-change events and multiple-change events. Single-change events refer to occurrences in which a pixel experienced only one change during the six periods of 1990-1995, 1995-2000, 2000-2005, 2005-2010, 2010-2015, and 2015-2020, while multiple-change events involve pixels that underwent two or more changes. This analysis aids in identifying hotspots of planted forest change in China during the last 30 years.

Planted forest C storage
This study used China’s 1:1,000,000 vegetation type map to estimate the C storage of planted forests in biomass using the C density method. To accurately estimate C storage in planted forests over different periods, we collected the seventh national forest inventory data (representing 2005 in this study), which records tree species, geographic allocation, and age information. Based on the forest inventory data, we determined C densities for various types of planted forests at different periods. We then estimated changes in C storage in aboveground biomass of planted forests by comparing C storage in patch locations with the same vegetation type (see Fig. 2), following the process outlined below:

(1) Acquisition of planted forest inventory samples for different forest types. We extracted planted forest samples from the forest inventory data based on forest type annotations. The planted forest samples were divided into 17 subsets (Supplementary Table 5) according to the forest types reported in China’s vegetation map.

(2) Acquisition of planted forest samples across various periods. We used the age information from the forest inventory data and remote sensing time-series analysis to construct planted forest inventory samples for the various vegetation types corresponding to different periods. Since the forest inventory data used in this study represent 2005, for 1990 to 2000, the samples were directly acquired according to the age variation in the inventory data. For instance, the inventory samples with a tree age exceeding 15 years represent they did not have a disturbance between 1990 and 2005, and can therefore be used to represent the 1990 sample, whose age is obtained by subtracting 15 years. For 2010 to 2020, we first used NDVI and forest time-series masks to assess the changes in each sample from 2005 to 2010, 2005 to 2015, and 2005 to 2020, preserving those that remained unchanged and discarding any that had altered. By employing these methodologies, we acquired inventory samples for each period, inclusive of information about the tree species and age.

(3) Estimation of C density. With the inventory sample collections established for each period, the age-biomass density equation (Eq. 1) at the tree species scale crafted by Xu, et al.58 was applied to compute the biomass density for each sample in all forest types, and the C density value for each sample was determined using the 0.5 biomass-carbon conversion factor suggested for China’s forests by Ma, et al.59. Subsequently, the average C density and its standard deviation for each type were calculated. It is important to note that this approach might overstate the C density of newly established planted forests between 2005-2020, as their ages ranged from 1-15 years, and the sample ages for 2010, 2015, and 2020 determined were all above 5 years. Consequently, for the newly added planted forests in this period, the study adopted the widely used space-for-time substitution approach60, selecting 2005 samples aged 1-15 years to estimate the C density of these new types of planted forests using the age-biomass equation (Eq. 1):

B = w / (1 + ke^(-at)) (1)

in which B is the biomass density (Mg C/ha), t is the forest age, w, k, and a are constants that were determined by Xu, et al.58.

(4) Calculation of C storage in planted forests. According to the forest types of the vegetation map, we first classified the planted forest area into 17 forest types based on the nearest neighbor principle. Utilizing the C densities of these vegetation types corresponding to disparate periods established (Supplementary Table 5), the C storage of China’s planted forests for these periods was calculated as (Eq. 2):

C = Σ (D_i × Area_i) (2)

where C is the total C storage in the biomass of the planted forests whereas D_i and Area_i are the C density and area of the i-th planted forest type. N is the number of types.

(5) Validation. To demonstrate the reasonableness of the calculated C storage in the planted forests, we used publicly accessible field survey samples created by Xu, et al.61 (download from the National Ecological Data Center resource sharing service platform of http://www.nesdc.org.cn/) to validate the estimated C storage in 2010, because these samples were collected around in 2010. Additionally, we downloaded China’s forest biomass data for the year 2019 generated by Yang, et al.62 (https://www.3decology.org/2023/08/02/china-forest-agb-map2019/) and the Global dataset of forest aboveground biomass for the year 2020 (https://climate.esa.int/en/projects/biomass/data/) to validate the estimated C storage in 2020. The comparison results indicated an R2 ranging from 0.50 to 0.68 (Supplementary Fig. 14).

Sources of errors and uncertainties
This research, together with our previous works23,24,36,62,63, revealed the dynamics of China’s planted forests and their C storage. However, some uncertainties remain in the results, mainly associated with mapped planted forest area and C density derived for vegetation types. Identifying them can help clarify the caveats associated with this study.

Errors in planted forest mapping originate from various sources. First, while the Landsat time series covers our study period adequately, the presence of cloud cover and shadows, combined with the extensive study area, made it difficult to obtain high-quality, cloud-free images for the entire country. This is the primary reason why our study could not generate annual planted forest maps. Therefore, to maximize mapping accuracy, we created cloud-free and shadow-free composites every five years. Nevertheless, even after cloud and shadow removal, some gaps still exist, with the largest being only 0.2% (Supplementary Fig. 15), which is almost certain not to affect the classification results. Second, our classification process constructed the optimal feature set for each period out of the 220 features we established using the feature selection method (Supplementary Note 3). Although our classification features encompassed spectral, textural, and terrain information, we may have overlooked variables that could impact the final classification outcome. Furthermore, we did not conduct a comparative analysis of different classification algorithms and chose to use the widely accepted RF algorithm. However, there might be other machine learning algorithms better suited for planted forest identification. Third, our classification samples originated from three sources: national forest inventory, field sample, and crowdsourcing. National forest inventory data are widely recognized as the most authoritative source1,58. Field samples were collected by forestry experts from various provinces, and the accuracy of their labels is generally reliable and commonly used for remote sensing mapping62. However, the crowdsourced data collection method presented challenges for our classification process. Although each crowdsourced sample underwent expert verification63, the subjectivity involved in determining pixel reference classes through photo interpretation by different experts may have introduced noise into the training and validation datasets52. However, this noise is expected to have a minimal impact on the final classification results64. Furthermore, we established samples for each period based on the NDVI time series of each sample, which may be affected by NDVI noise. To quantify the uncertainties of our resultant maps, we mapped the distribution of pixel uncertainty (Supplementary Note 5 and Supplementary Fig. 16), revealing that low and medium uncertainties exist in large areas of the dataset (>80%) (Supplementary Fig. 16). Less than 20% of the total area in the southwestern and northeastern regions was classified with relatively high uncertainties (Supplementary Fig. 16).

Errors in C storage estimation primarily stem from the following aspects. First, we used a 1:1,000,000 scale vegetation map of China to classify planted forests into 17 types for each period. However, the vegetation map used in this study was created between the 1980s and the 2000s, and over several decades, China’s vegetation formations have undergone significant changes24. Therefore, there might be some misclassification when using this map for vegetation type classification. Nevertheless, the 1:1,000,000 scale China vegetation map represents the potential distribution regions of Chinese vegetation65. Although some species may have changed over the years, vegetation types and their general distribution have remained relatively stable compared to the 2020 updated vegetation map23. Hence, in this study, we employed a nearest-neighbor approach to classify planted forests into 17 types according to the forest types of vegetation map and then calculated the C storage. We also compared the classification results with the updated 2020 China vegetation map23, which revealed that over 80% of grids had the same vegetation types (Supplementary Fig. 17). For C storage estimates, only 2% had a difference greater than 0.1 (Supplementary Fig. 17). Second, we estimated C densities for different forest types using national forest inventory data and referring to the age-biomass density equations developed by Xu, et al.58. We generated vegetation type C density datasets for different periods by considering forest age and change detection methods based on the forest inventory data. Although the forest inventory data used in our study were collected between 2004 and 2008, for consistency with our research data, we treated them as representative of the year 2005. As a result, this may introduce an error of 1-3 years in forest age, impacting the C density estimation. Additionally, this method might have resulted in an overestimation of C density for newly established planted forests after 2005. This is because, after 2005, the minimum forest age for each period (including 2010, 2015, and 2020) exceeded 5 years. Therefore, we adopted a space-for-time approach to select samples with forest ages between 1 and 15 years in 2005 and calculated the C density for these newly established planted forests after 2005.

Finally, to quantify the uncertainty of C estimation, we calculated the mean and standard deviation of the C density of each forest type based on the forest inventory samples. The mean value was used to estimate C storage, and then we calculated the total uncertainty by calculating the C storage in planted forests using the lower and upper standard deviation values to provide a measure of uncertainty associated with the total C storage of planted forests66.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript addressed the quantification of areal extent and carbon storage of planted forests in China from 1990 to 2020. The detection of planted area is achieved with Landsat images. Classification and validation of the newly planted forest maps are supported by an extensive dataset of sample measurements and auxiliary datasets. Dynamics of land cover are analyzed and attributed to afforestation and reforestation programs. The planted forest maps are then related to measurements from the National Forest Inventory to quantify their carbon storage and their potential uptake of carbon.\n\nThe manuscript is in principle of interest to readers of Nature Communications because it provides a detailed explanation of land cover trajectories in China during the last 30 years. It is acknowledged that this is a massive study, supported by many figures and tables in the Supplement. It is a strenuous task to bring all the evidence together in a single manuscript. However, I find the manuscript overloaded with topics, with a tendency towards the remote sensing component, which in turn makes the manuscript less appealing to this journal in my opinion. Aspects related to the implications of the findings are less pronounced, giving the feeling that the manuscript has not reached its natural balance yet.\n\nBased on my background on remote sensing, I also see several aspects that need to be clarified concerning the derivation of the forest planted maps, their accuracy and the quantification of the carbon storage. To appreciate whether the numbers provided are reasonable, a thorough analysis of errors is needed in addition.\n\nThe authors find below a set of general comments that may stimulate some thinking on how the manuscript shall be improved and a more detailed list of comments having the aim of polishing the text. The authors are strongly encourage to revise their manuscript and submit a new version to Nature Communications.\n\nGeneral comments\nThe aim apparently was to monitor the carbon storage dynamics. This aspect is vaguely addressed as it is not clear how C was validated and what is the magnitude of the error bars (text on lines 349-357 is not satisfactory).\n\nThe mapping sequence is not entirely clear. This is my understanding:\n1) A forest mask for each epoch was created from existing land cover maps. Question here is how was “forest” defined. Could it be that the definition behind each dataset was different, leading to different accuracy statistics which then influenced the selection of the reference for the forest mask?\n2) Planted forest and natural forest maps (yearly? for every five years?) were obtained from Landsat metrics + auxiliary datasets in correspondence of the forest mask. When evaluating these maps, user/producer accuracies are provided for the two classes but the manuscript discusses conversions from shrubland, cropland and other classes to/from natural and planted forest. How where the non-forest classes mapped? What is the accuracy statistics for these classes?\n\nA flowchart would very much help.\n\nSeveral comparisons are mentioned throughout the manuscript but no figure compares values (see also detailed comment for line 122). The number of figures could actually be reduced if reference data and mapped data were co-plotted. This would avoid jumping back and forth in the manuscript and let the reader appreciate how well the mapped data corresponds to the reference.\n\nThe Section on “Present and future planted C forest storage potential related to water availability” has certain merits but seems to be out of scope in this manuscript and should be reconsidered. I would focus on a transparent presentation of the planted forest maps (strengths and weaknesses), and reinforce the text describing the C balance of planted forests with implications in terms of policy and future developments with respect to future forest expansion programs (if any of course).\n\nThe title of the Section “The largest LULC conversion in human history” is not acceptable and needs to be reconsidered. Do the authors have any evidence that historically there was not any other conversion of larger extent? Secondly, this section is pretentious and does not embed results in the context of a real discussion. Finally, Fig. 4 is out of place; it should be presented before the discussion.\n\nThe Section “Planted forest as a potential forest C sink” is disappointing. It is currently a jumble of possible explanations for the sink of planted forest while no mention is made of uncertainties.\n\nThe Section “Challenges and implications for the China’s planted forest” is probably the most relevant part to readers of Nature Communications. Nonetheless, it only addresses one Figure placed in the Supplement and does not relate to results in the main body of the manuscript.\n\nThe last sentence is indicative of the level of maturity of this manuscript. The authors engaged in a remarkable exercise of data collection, data crunching, map verification but did not convey the message of the actual value of their findings. The actual value comes from a production of results and a thoughtful interpretation of results, including those that are less convincing. This latter aspect was not deal with properly in the manuscript and I would see quite some efforts spent here to balance things out and provide a more objective perspective on the scientific results of this study.\n\nFurther to the presentation of errors, I find the role of uncertainties being underplayed in the overall evaluation of the results. Lines 556-569 state that most changes are improbable from a statistical point of view, which puts the question whether the magnitude of the changes and the pathways of conversion are correct. Indeed, I did not seem to find an external validation of these pathways in the manuscript (but just some total numbers) so it is hard to judge whether the numbers produced by this study are reliable. I truly hope that the authors can challenge my understanding here (see also comment below to lines 153-156, Supplementary).\n\nWould it be possible to add some insets of maps at full resolution in figure showing maps of planted forests for the whole of China?\n\nDetailed comments\nLine 45. How to access references 12, 13 and 14?\nLine 74. “600,000 field samples”. From methods and supplement I understood that these are not all “field” but several are photo-interpreted. Am I right?\nLines 71-77 and 79-86. These paragraphs are similar, consider merging.\nLine 113. What does “total changed pixels” mean?\nLine 117. What does “net increase area” mean?\nLines 119-120. What is meant with “indicating a decrease in overall net increasing planted forest area in most regions of China”?\nLine 122. The comparison referring to Supplementary Fig. 1 is unclear. This figure illustrates the extent of the planting programs and areal extent per year. What is the source for the areal extent? National statistics or this study? Personally, I would rather appreciate at this stage a scatter plot that compares values considered as reference and values obtained in this study.\nLine 161-162. I am intrigued by the conversion from natural forest to planted forest. What is the reason? This indeed is neither afforestation nor reforestation or am I wrong?\nLine 170. What is the added value of Supplementary Fig. 10 when compared to Fig. 2a in this context?\nLines 174-178. What are multi-change events crop-to-forest?\nLine 187. What does “C storage leading by planted forest area expansion” mean?\nLine 191. Are 8 TgC/year the result of 240 TgC / 30 years? This is rather simplistic as it assumes a linear rate which I did not see in the manuscript. In any case, I would appreciate a confidence interval on the total.\nLine 192. 11% is with respect to all forests in China (natural+planted)?\nLine 195. What is 99% referring to?\nLines 196-198. The sentence needs to be revised. What do “obviously increase”, “largest three net accumulation” and “declined area” mean?\nLine 203 (and then again line 601). The statement “canopy height …. relates linearly with C density across large scales” is indefinite and partly incorrect. The reference to Fang et al., 2006, clearly shows a non-linear trend from short to tall forest (Fig. 1 therein). A linear trend characterized forests taller than 10 m, which may not be the case for newly planted forest as those addressed in this manuscript.\nLine 205 (and then again line 606). Claiming that P-PET is the best predictor for forest canopy height is challenged by the plot in Figure 3. The best predictor would be spaceborne LiDAR interpolated with some kind of spatial predictor in my opinion, or am I missing something?\nLine 220, Fig. 3. What is the spatial scale, i.e., spatial resolution, of the P-PET data layers and the canopy height? Are these comparable?\nLine 223, caption to Figure 3b, should refer to canopy height rather than P-PET.\nLines 225-229. Replace links with footnotes or cite elsewhere.\nLine 266. Why until 2014?\nLine 351. “mean values” of what?\nLine 354. “change events” are by definition “dynamic”. Please revise wording here.\nLines 473-490 (Field samples) and lines 492-504 (Landsat data) should be switched.\nLine 474. Of the 600,000 samples, only about 180,000 were explained. What is the origin of the remaining 420,000 samples?\nLine 475. What are “multi-source ways”?\nLine 474-475. If the samples were collected after 2003, which data were used to train the classifier to derive maps for the time period 1990-1995-2000?\nLine 477. How were the crowdsourced samples used? They were collected since 2018 so in theory useful only to support maps for 2020.\nLine 484. Please clarify here and throughout the whole text including the Supplement whether the forest masks were annual or quinquennial.\nLine 486. What does “target regions” mean?\nLine 488. How where “significant changes” defined?\nLine 502-504. Do I understand correctly that the mosaics are not gap-free? If so what is the size of the gaps? Is this relevant at all?\nLine 508. Which “scales” are you talking about?\nLine 509-510. Why would you compare a map based on data for 2004-2008 with your planted forest maps representative of the epochs 2000 and 2010?\nLine 513. “temporal features” of what?\nLine 510. “Surface reflectance data” is it the Landsat-based?\nLine 523 “Median value” of what?\nLine 529. It is referred to Supplementary Note 2, which however repeats what has been reported in this paragraph.\nLine 544. Of the “four” validation methods, the two last ones are just a comparison. Please rephrase.\nLine 553. “agreed well” should be replaced with a more precise statement.\nLine 594. Could you please elucidate on the link between vegetation type map and the maps of planted forest.\nLine 620. Does not Nature Communication have a policy that all data material should be open and public? If not, which “data” will be made available. I think it is responsible act of transparent science to release maps and accounts.\n\nSupplementary Notes\nLines 20-21. How could the maps of 1990, 1995 and 2000 be assessed if no data were available before 2003?\nLine 21. Figure 23 shows overall accuracies but the real interest here is on a confusion matrix listing all classes that were mapped.\nLine 43. Please provide a reference for the harmonic analysis.\nLine 61. What is an “original website”?\nLine 74. What does “exported overall accuracies” mean?\nLine 91 and line 114. “validation data from stratified random sampling” is unclear. Please specify instead the origin of the data\nLine 94. “maps and datasets from other studies” is generic. Please specify.\nLine 106-111. I am not sure that Supplementary Fig. 12b and Fig. 14 are correctly referred to here.\nLines 111-112. Can you please explain why accuracy increased with time? Is it a consequence of the higher quality of the Landsat data?\nLines 117-120. I am puzzled by the “random generation of points” in a “grid with resolution of 0.1 x 0.1 deg”. Could you please clarify?\nLines 122-126. Where are the accuracies shown?\nLine 130. Are the statistical data published on a yearly basis? What about the period 1990-2000?\nLine 132. The large dispersion of data points in Supplementary Fig. 18 should be addressed as well.\nLine 142. What exactly was digitized here?\nLine 143. What is Supplementary Fig. 19 supposed to show? As far as I can judge, the figure does not show agreement. It is a comparison of totals. Both maps could be wrong in how planted forests are distributed but the totals agree. Any thoughts on how to address this issue?\nLines 153-156. Claiming that an uncertainty < 0.4 is low-to-moderate (rather than “medium”) whereas it is high when > 0.4 is misleading. It is generally agreed upon that high accuracy is > 0.7. If this understanding is correct, the planted forest maps are highly uncertain, with some relevant impact on the relevance and the outcome of this study. Please clarify.\nLine 308. “Samples” are difficult to discern when the figure is printed on paper.\nLine 322. What is the reason for adding regression lines here? These lines indicated that the slope and the intercept increase with time. Could it be a symptom that the maps suffer from the quality of the Landsat data? In principle, forest patches of a certain size should always be mapped at a certain size +/- some uncertainty whereas here we observe a clear temporal trend.\nLine 326. What is shown in these maps?", "reviewer_2": "This could be a data paper, but it falls short of a research paper targeting carbon stock assessment. The author used Landsat TM images and forest samples to generate planted forest maps in China from 1990 to 2020. Despite the fact that I believe the planted forest maps do add new knowledge to our understanding, the focus of this study - carbon stock assessment - was too simplified to be convincing.\n\nFirst, the carbon density of each vegetation type was obtained from the study of Lai et al (2016) (note that it is not Li et al, I believe). This inherits the uncertainties from the study (Lai's study used mean values for the same vegetation type compiled from different studies). Besides, forest types are not fixed over time. Instead, they evolve and change during succession. Using a fixed type to extrapolate the carbon potential will be biased.\n\nSecond, using tree height as a proxy to project carbon density is also problematic. Indeed, there is a close relationship between tree height and P-PET. However, tree height is determined by site index, and factors such as soil fertility, slope, and aspect are important. Moreover, height is also related to the planting density of the trees. Using a simplified relationship of height and P-PET to further extrapolate the carbon stock potential is highly unreliable.\n\nBecause of the above-mentioned problems, I highly doubt the reliability of the discussion and conclusions. For example, in lines 322-325, this is not reasonable as there are no such absolute differences between planted and natural forests. An instance is that human interventions to expedite the succession process via complementary planting of seedlings, selective cutting, and pruning in natural secondary forests will also help increase carbon stock. Are those forests natural or planted?", "reviewer_3": "Comment for authors：\nThis manuscript obtained planted forest maps for each five-year interval from 1990 to 2020 based on Landsat data and random forest algorithm, and analyzed the sources of planted forest expansion in China. Meanwhile, this study quantified the C storage resulting from the expansion of planted forest over the past 30 years, and evaluate the biomass C storage potential of the additional planted forest. Overall, this study is truly unprecedented, carrying milestone significance in enhancing our understanding of the temporal and spatial changes within China's planted forests and their pivotal role in mitigating global climate change. However, there are some minor issues that need to be solved before publication.\n\nMain Comments:\n1、The manuscript mentions that canopy height is a significant factor related to forest carbon storage potential. However, it appears that the author did not take canopy height into account when calculating the carbon storage conversion from other land types to planted forests. Instead, the author calculated carbon storage solely based on the conversion area. This oversight raises questions about the accuracy of the carbon storage estimate.\n2、In light of the points mentioned above, we assert that the results pertaining to P-PET in the final section (Lines 202-219) have limited relevance to the overall content. Their removal will not disrupt the paper's logical structure but will enhance its conciseness.\n3、In the analysis of Land Use and Land Cover (LULC) transfer, the author identifies shrubland, grassland, and natural forest as the primary sources of plantation forest. While the author provides comprehensive explanations and references for shrubland and grassland in the discussion, there appears to be a gap in addressing the key issue of natural forest loss, which is arguably the most critical forest type to protect. It is evident that a more in-depth discussion regarding the reasons behind the loss of natural forests is warranted.\n4、The quality and consistency of the two vegetation-type maps are crucial factors influencing the reliability of the results concerning carbon storage changes. I have concerns regarding the consistency in classifying vegetation types within these two maps. If different classification systems were employed, it would render them non-comparable. Therefore, it is imperative that the author provides more detailed information about these two vegetation maps.\n\nMinor Comments:\n1、Line 290, native forest should be natural forest;\n2、Line 304 underscores should be underscore.\n3、The contents of subgraph b, and d of Fig 3 are not corresponding with the title of the subgraph.\n4、Line 541, it is suggested to add respectively at the end of the sentence to increase readability.\n5、Line 502, the full name of the FMASK algorithm should be given at the first time.\n6、Line 539 to 541, the classification model adapted to each region and period should be provided.\n7、Line 557, work should be works.\n8、Line 587, delete word code.\n9、Line 589, vegetation-type maps should be vegetation-type map."}, "author_response": "Response to Reviewers\nReviewer #1:\nThis manuscript addressed the quantification of areal extent and carbon storage of\nplanted forests in China from 1990 to 2020. The detection of planted area is achieved\nwith Landsat images. Classification and validation of the newly planted forest maps\nare supported by an extensive dataset of sample measurements and auxiliary datasets.\nDynamics of land cover are analyzed and attributed to afforestation and reforestation\nprograms. The planted forest maps are then related to measurements from the\nNational Forest Inventory to quantify their carbon storage and their potential uptake\nof carbon.\n\nThe manuscript is in principle of interest to readers of Nature Communications\nbecause it provides a detailed explanation of land cover trajectories in China during\nthe last 30 years. It is acknowledged that this is a massive study, supported by many\nfigures and tables in the Supplement. It is a strenuous task to bring all the evidence\ntogether in a single manuscript. However, I find the manuscript overloaded with\ntopics, with a tendency towards the remote sensing component, which in turn makes\nthe manuscript less appealing to this journal in my opinion. Aspects related to the\nimplications of the findings are less pronounced, giving the feeling that the\nmanuscript has not reached its natural balance yet.\n\nBased on my background on remote sensing, I also see several aspects that need to\nbe clarified concerning the derivation of the forest planted maps, their accuracy and the\nquantification of the carbon storage. To appreciate whether the numbers provided are\nreasonable, a thorough analysis of errors is needed in addition.\n\nThe authors find below a set of general comments that may stimulate some thinking\non how the manuscript shall be improved and a more detailed list of comments having\nthe aim of polishing the text. The authors are strongly encouraging to revise their\nmanuscript and submit a new version to Nature Communications.\n\nResponse: Thank you for your valuable and positive feedback. Your insightful\ncomments have been instrumental in enhancing the quality of our work. We have\ncarefully considered each of your comments and made substantial revisions to the\nmanuscript. Below, we provide comprehensive point-by-point responses to address\nthe specific issues raised during the review process.\n\nGeneral comments\nvaguely addressed as it is not clear how C was validated and what is the magnitude of\nthe error bars (text on lines 349-357 is not satisfactory).\n\nResponse:We agree with the comment that the reviewer pointed out. We have\ncarefully addressed these issues in our revised manuscript.\nFirst, our primary aim in this study was indeed to monitor the spatiotemporal\ndynamics of carbon storage in China’s planted forests. To address this aspect more\nexplicitly, we have extensively revised the introduction section of the paper to\nemphasize the core objectives of our study. We kindly invite you to review the\nmodified introduction in our revised manuscript in Line 40-50 for a more precise\npresentation of the research theme.\nSecond, we have provided detailed information on the validation of carbon storage\nestimates using field plot (Xu et al., 2019) and Yang’s biomass map (Yang et al.,\n2023), suggesting an R2 of 0.6 to 0.7 (Fig. R1). Based on forest inventory data, we re-\ncalculated the carbon density for different vegetation types (Methods Line 486-541,\nSupplementary Table 6), and described magnitude of error for carbon storage\nestimates (Fig. R2 and Fig. R3). These error calculations have been rigorously\nconducted through robust statistical analysis to ensure their reliability. Based on the\nnew results, we analyzed the carbon storage change of planted forest from 1990 to\n2020 (Line 143-248).\nFurthermore, we have delved into an in-depth analysis of the uncertainties about\ncarbon storage estimation. We have discussed potential sources of uncertainties and\nhighlighted the proactive measures taken in our study to minimize these uncertainties\n(Line 549-614). We believe that these modifications and additions have significantly\nimproved the clarity, credibility, and completeness of our research.\n[figure redacted]\nFig. R1. Comparison with Yang’s biomass dataset (a) and field survey(b).\nFig. R2 Changes of C in planted forest biomass over national and regional scales.\nFig. R3. C storage changes during various periods at different regions.\n1) A forest mask for each epoch was created from existing land cover maps. Question\nhere is how was “forest” defined. Could it be that the definition behind each dataset\nwas different, leading to different accuracy statistics which then influenced the\nselection of the reference for the forest mask?\n\nResponse: The used three land cover dataset (GLC_FCS30 generate by Zhang et al\n(2021), CLCD generated by Yang and Huang et al (2021), and CLUD generate by Liu\net al (2014)) had different forest definitions. We acknowledge that this difference in\nforest definition could influence accuracy statistics and, consequently, the reference\nselection for the forest mask. In our study, based on a straightforward accuracy\ncomparison, we selected the annual China Land Cover Dataset (CLCD) (Yang et al.,\n2021) as the foundational dataset.\nIt is essential to note that our validation samples were entirely independent and\ncollected based on field samples. These samples faithfully represent on-ground\nsituations and provide an accurate reflection of the actual land cover types. Thus, we\nleveraged these independently collected samples to assess the accuracy of each forest\nmask and determine which definition aligns better with the China’s forest distribution.\nAs a result, the dataset we selection is rational.\nWe appreciate the reviewer's attention to this matter and hope that this explanation\nclarifies this concern.\n\n2) Planted forest and natural forest maps (yearly? for every five years?) were obtained\nfrom Landsat metrics + auxiliary datasets in correspondence of the forest mask. When\nevaluating these maps, user/producer accuracies are provided for the two classes but\nthe manuscript discusses conversions from shrubland, cropland and other classes\nto/from natural and planted forest. How where the non-forest classes mapped? What\nis the accuracy statistics for these classes?\nA flowchart would very much help.\n\nResponse: We generated planted and natural forest maps for the years of 1990, 1995,\n2000, 2005, 2010, 2015, 2020 based on correspondent forest masks of CLCD. We did\nnot identify other land cover types such as cropland, grassland, shrubland, etc.\nInstead, we directly used the land cover data from CLCD for the analysis of\nconversion in cropland, grassland, shrubland, etc. We added a flowchart (Fig. R4) to\ndescribe the planted forest mapping procession, mainly include selecting forest mask\nselection, generate training and testing sample for different periods, and mapping.\nFig.R4. Flowchart of mapping planted forests.\ncompares values (see also detailed comment for line 122). The number of figures could\nactually be reduced if reference data and mapped data were co-plotted. This would\navoid jumping back and forth in the manuscript and let the reader appreciate how well\nthe mapped data corresponds to the reference.\n\nResponse: We are very grateful for the reviewer's suggestions. Following the reviewer's\ncomments and the newly revised manuscript, we have corrected the figures and tables,\nparticularly those in the manuscript file. The figures in the Supplementary have also\nbeen modified. Please check these revisions.\nwater availability” has certain merits but seems to be out of scope in this manuscript\nand should be reconsidered. I would focus on a transparent presentation of the planted\nforest maps (strengths and weaknesses), and reinforce the text describing the C\nbalance of planted forests with implications in terms of policy and future\ndevelopments with respect to future forest expansion programs (if any of course).\n\nResponse: We highly agree with the reviewer's suggestion. After integrating the\ncomments from you and other reviewers, the “Present and future planted C forest\nstorage potential related to water availability” section has been removed from\nmanuscript. In line with the reviewer's suggestions, we focused on carbon storage\nchanges resulting from spatiotemporal dynamic of China’s planted forests. Therefore,\nwe have added more results and discussions regarding carbon storage estimates,\nchanges, and uncertainty analysis in revised manuscript. Please refer to the response\nto first General Comment.\nacceptable and needs to be reconsidered. Do the authors have any evidence that\nhistorically there was not any other conversion of larger extent? Secondly, this section\nis pretentious and does not embed results in the context of a real discussion. Finally,\nFig. 4 is out of place; it should be presented before the discussion.\n\nResponse: We agree and have deleted it. Meanwhile, we re-organized the discussion\nsection according to the updated results. Please see the revised manuscript in Line\n255-269.\ncurrently a jumble of possible explanations for the sink of planted forest while no\nmention is made of uncertainties.\n\nResponse: Agree, please see the response to fourth General Comment.\nprobably the most relevant part to readers of Nature Communications. Nonetheless, it\nonly addresses one Figure placed in the Supplement and does not relate to results in\nthe main body of the manuscript.\n\nResponse: Following your suggestion, we re-organized the discussion section, and\nstrengthened the discussion about the results. Please see Discussion Section in Line\n249-356.\nauthors engaged in a remarkable exercise of data collection, data crunching, map\nverification but did not convey the message of the actual value of their findings. The\nactual value comes from a production of results and a thoughtful interpretation of\nresults, including those that are less convincing. This latter aspect was not deal with\nproperly in the manuscript and I would see quite some efforts spent here to balance\nthings out and provide a more objective perspective on the scientific results of this\nstudy.\n\nResponse: Thank you for the professional suggestions. As mentioned in response\nabove, the revised manuscript focused on the spatiotemporal dynamics of carbon\nstorage in China’s planted forests. In Discussion Section, we have primarily discussed\nthe reasons for the spatiotemporal dynamic of China’s planted forests, and the\nresultant increase in planted forest carbon storage from the planted forest expansion in\narea and its growth, as well as the uncertainties associated with planted forest maps\nand results of carbon storage. Finally, we concluded “Our study is the first to\ncharacterize the spatiotemporal dynamics of C storage in the biomass of China’s\nplanted forests over a thirty-year period, from 1990 to 2020. We found that the\nincrease in C storage within China’s planted forests can be primarily attributed to the\nexpansion of planted forest areas. This expansion has largely occurred through the\nconversion of croplands, shrublands, and grasslands into planted forest areas. In\naddition to area expansion, the growth of China’s planted forests themselves\nrepresents another significant factor contributing to C storage. Post-2000, a policy-\ndriven push for expanding planted forest areas has led to rapid increase, with the\nmajority of these planted forests currently in their young or middle age. Based on\nthese results, we anticipate that the newly expanded planted forests will possess\nsubstantial C storage potential in the future, potentially playing a key role in helping\nChina meet its Carbon Neutrality Target by 2060.”. Please see Discussion Section in\nLine 358-369.\nunderplayed in the overall evaluation of the results. Lines 556-569 state that most\nchanges are improbable from a statistical point of view, which puts the question\nwhether the magnitude of the changes and the pathways of conversion are correct.\nIndeed, I did not seem to find an external validation of these pathways in the\nmanuscript (but just some total numbers) so it is hard to judge whether the numbers\nproduced by this study are reliable. I truly hope that the authors can challenge my\nunderstanding here (see also comment below to lines 153-156, Supplementary).\n\nResponse: Thanks for pointing this out. We supplemented more details about the error\nand uncertainty analysis. First, we recalculated the carbon density for various\nvegetation type, and their standard deviations (Methods about planted forest carbon\nstorage in Line 486-541, and Supplementary Table 6), and added the error range to\nanalysis the results of carbon storage dynamics step by step (Results section of line\n143-248). Second, we validated results of the carbon storage estimation using field\nsurvey and existing remote sensing product, indicating an R2 of 0.6 to 0.7 (Fig R1).\nThird, we conducted an in-depth analysis about the source of uncertainties to results\nof the carbon storage estimation (Line 543-614).\nshowing maps of planted forests for the whole of China?\n\nResponse: Following your suggestion, we added planted forest maps with 30m\nresolution in Supplementary Fig.1 as shown in Fig.R5.\nFig.R5. Planted forest maps from 1990-2020\n\nDetailed comments\nResponse:These three references are Chinese government reports, if you are\ninterested in these three reports, please contact the corresponding author.\nthese are not all “field” but several are photo-interpreted. Am I right?\nResponse: Yes, the crowdsourced samples were photo-interpreted by vegetation\necology experts.\nResponse:Thanks. We merged these two sentences, please check in line 78-79 of\nMain Text.\nResponse: We conducted a spatiotemporal analysis of planted forest area changes at a\n0.1° grid scale. Here, \"total changed pixels\" refers to all the grid cells where changes\nin plantation forest area have occurred. To avoid misunderstanding, we revised this to\n“changed pixels”. Please see line 119-120, and 123.\nResponse:\"Net increase area\" refers to the increase in planted forest area over a\nperiod. For example, it is calculated by subtracting the planted forest area in 1990\nfrom the planted forest area in 1995, representing the net increase in area over those\nfive years.\nplanted forest area in most regions of China”?\nResponse:We conducted trend analysis using linear regression within the 0.1° grid\ncells for the increase in planted forest area every five years. This means that the\nresults indicate that in most grid cells, there is a decreasing trend in the increase of\nplanted forest area every five years.\nillustrates the extent of the planting programs and areal extent per year. What is the\nsource for the areal extent? National statistics or this study? Personally, I would rather\nappreciate at this stage a scatter plot that compares values considered as reference and\nvalues obtained in this study.\nResponse:The data in Supplementary Fig. 1 is sourced from national forest statistics.\nWe have compared the plantation forest area obtained in our study with national\nstatistical data from 2000 to 2020, as shown in the following Fig.R6. Our data shows\na good level of consistency with the statistical data, with an R2 ranging between 0.76\nand 0.87.\nFig. R6. Comparison with statistical planted forest area.\nforest. What is the reason? This indeed is neither afforestation nor reforestation or am\nI wrong?\nResponse: This is a highly intriguing and worth addressing question. In China, the\ntransformation of natural forests into planted forests is primarily influenced by a dual\nimpact of policy-driven and economic factors. Since the 1970s and 1980s, China has\nseen extensive deforestation of natural forests to meet the demands of national\neconomic development, resulting in a decline in the quality of natural forests (Cao et\nal., 2011). In response to the deteriorating state of natural forest ecosystems, the\nChinese government has successively introduced national and provincial-level\npolicies for the protection of natural forests (Chen et al., 0222). These policies\npromote afforestation to enhance the structure of natural forests, leading to the\ngradual conversion of some lower-quality natural forests into planted forests (Cao et\nal. 2011). Economic development requires a supply of timber and related products. To\nmeet these economic development needs, particularly in the southeastern tropical\nforest regions of China, there are many cases of natural forests being converted into\nChinese fir planted forests (Yang et al. 2019). In addition, the establishment of fast-\ngrowing and high-yield planted forests for economic purposes has also led to the\nloss of natural forests (Zhai et al. 2017). However, since 2015, China has ceased\ncomprehensive commercial logging, and the area of natural forests has slowly\nincreased, as evidenced by national statistical data.\nFig. 2a in this context?\nResponse: Supplementary Fig. 10 illustrated the predominant types of land cover\nconversion in different regions for change events (single and multiple events),\nwhereas Fig. 2a present the primary land cover types converted into plantation forests\nfrom 1990 to 2020, which differs from the information in Supplementary Fig. 10. We\nhave revised the order of figures in manuscript, the Fig.2a has been adjusted to Fig.\n4a. Please check in Line 238-244.\nResponse:To interpret this transformation, it is necessary to consider China's\nafforestation policies. Firstly, the GFG has been a significant factor driving the\nexpansion of China’s planted forests. However, after afforestation, some regions may\nexperience improper management of planted forests, leading to their mortality and\nnecessitating reforestation, resulting in interactive changes. Research indicated that\nfrom 1952 to 2005, the overall survival rate of trees in afforestation projects was only\n24% (Wang et al., 2007), with a survival rate of only 15% for the Three-North\nShelterbelt Program (Li, 2001, Cao, 2008). This has resulted in multiple rounds of\nland cover type changes between planted forest and other land use types. China has\ninnovated afforestation techniques, such as runoff forestry and deep planting, which\nintegrate seedling and container-based methods, are actively being pursued to enhance\nthe drought resistance and survival rates of planted forests (State Forestry and\nGrassland Administration of China, 2019).\nResponse:This subheading was intended to emphasize the contribution of the\nexpansion of China’s planted forest area over the past three decades to the increase in\ncarbon storage in planted forests. To reduce ambiguity, we have deleted this\nsubheading.\n\n22 191. Are 8 TgC/year the result of 240 TgC / 30 years? This is rather simplistic as it\nassumes a linear rate which I did not see in the manuscript. In any case, I would\nappreciate a confidence interval on the total.\nResponse:Thank you for the useful suggestions which we considered carefully. To\nobtain a more precise estimate of the changes in carbon storage in China’s planted\nforests, we have conducted a comprehensive effort about China’s planted forest\ncarbon storage estimates.Please refer to the response to first General Comment.\nPlease check in Line 143-248.\n\n23 192. 11% is with respect to all forests in China (natural+planted)?\nResponse: We have revised this section. Please see detailed in 143-185.\nResponse: In the previous manuscript version, the aim was to express that carbon\nstorage from the conversion of cropland, shrubland, grassland, and natural forests into\nplanted forests constituted 99% of the total increase in carbon storage in planted\nforests. As we have conducted a reevaluation and provided error estimates, these\nresults have consequently been revised. Please check in line 186-244.\n\n25 196-198. The sentence needs to be revised. What do “obviously increase”,\n“largest three net accumulation” and “declined area” mean?\nResponse:Thanks for your suggestion. We have corrected it and please in line 186-\n244.\nlinearly with C density across large scales” is indefinite and partly incorrect. The\nreference to Fang et al., 2006, clearly shows a non-linear trend from short to tall forest\n(Fig. 1 therein). A linear trend characterized forests taller than 10 m, which may not\nbe the case for newly planted forest as those addressed in this manuscript.\nResponse:We highly agree with the reviewer's suggestion and thank you for pointing\nout this issue. We only discussed this issue in line 321-325, which focused on the\nfavorable climatic contributing to the growth and survival rate of planted forest.\nPlease also refer to the response to first and fourth General Comment.\nforest canopy height is challenged by the plot in Figure 3. The best predictor would be\nspaceborne LiDAR interpolated with some kind of spatial predictor in my opinion, or\nam I missing something?\nResponse:Thank you again. Please refer to the response to first and fourth General\nComment, and the last response to line 238.\nlayers and the canopy height? Are these comparable?\nResponse:We analyzed the P-PET and carbon storage within the same 0.1° grid.\nResponse: We agree. However, we deleted this figure according the comments of all\nreviewers. Please see the response to General Comment 1, 4 and response to line 238\nand 240.\nResponse:This figure has been deleted according to the comments from you and\nother reviewers.\nResponse:There is no publicly accessible data after 2014.\nResponse: Mean values of carbon density for the same vegetation type.\nhere.\nResponse:Thanks for the suggestion. We have revised the content and defined in line\n478-484.\nswitched.\nResponse:Thanks for the suggestion, we switched these two sections. Please see line\n381-416.\norigin of the remaining 420,000 samples?\nResponse: I am sorry for the ambiguity. Among the 600,000 field samples, not only\nforest samples are included, but also other vegetation type survey data, such\ngrassland, shrubland etc. We only focused on forests, so we used 180,000 field\nsamples related to forests\nResponse:This research acquired more than 600,000 vegetation survey datasets\nthrough crowdsourcing, field surveys, and national forest inventories. To avoided\nambiguity, we revised the line 397-398.\ntrain the classifier to derive maps for the time period 1990-1995-2000?\nResponse: Utilizing the yearly CLCD forest mask data and NDVI temporal series\nfrom 1990 through 2020, we performed a change analysis on each sample. Samples\nthat exhibited no change were chosen as the reference samples for identifying planted\nforests for each period, from which we derived our training and verification samples\nfor every interval.The process of constructing samples for various periods has been\nadded; for details, see lines 394-416 in the revised manuscript.\n2018 so in theory useful only to support maps for 2020.\nResponse:Similar to response to Comment 37. By conducting a time-series change\nanalysis on crowdsourced samples, those without changes are used as the reference\ndataset for each period, further divided into training and validation samples.We have\nadded methodological details, and please refer to lines 394-416.\nSupplement whether the forest masks were annual or quinquennial.\nResponse:We appreciate the recommendation. Explanatory notes have been\nincorporated throughout the revised manuscript and supplement to clarify that the\nforest masks (CLCD) pertain to each year from 1990 to 2020. Please see line 404-405.\nResponse:They are the forest regions without changes. We revised it and please see\nline 394-416.\nResponse:Here, referencing the research of Khunrattanasiri et al. (2022), we\ndetermine whether each sample has undergone changes based on whether the\nminimum value of the NDVI time series for each sample is less than 0.6.To prevent\nmisinterpretation, modifications and clarifications have been made in the manuscript;\nplease refer to lines 410-412.\nwhat is the size of the gaps? Is this relevant at all?\nResponse: We computed the quantity of null values present in the five-year composite\nimagery, and the findings indicated that post-cloud processing, null-value pixels in\nforest regions between 1990 and 2020 accounted for merely 0.1% to 0.2% (Fig. R7),\nsignifying a minimal effect.In our discussion of uncertainties, we have augmented the\nsection to address the impacts arising from image quality and missing data; see lines\n549-557 for this detail.\nFig. R7. Landsat gaps for various periods\nResponse:The “scales” mean national and provincial scales, we revised to “national\nand provincial scales” in Line 455.\nyour planted forest maps representative of the epochs 2000 and 2010?\nResponse: This map, dating from 2004 to 2008, was digitized using data from a\ncomprehensive survey of forests in China. It is currently the only dataset that\naccurately reflects the distribution of China's planted forests. This dataset has been\nwidely utilized in various research studies, such as the work conducted by Peng and\nothers. To facilitate a meaningful comparison with this dataset, we selected the\ngenerated planted forest maps in 2000, 2005, and 2010 to compare with this dataset,\nbecause they are close in year.\nResponse:The term \"temporal characteristics\" refers to the features associated with\nthe time series changes of vegetation indices. In this study, we primarily utilized\nmetrics such as amplitude and phase to analyze these temporal characteristics. See\ndetails in Supplementary Note 2.\nResponse:Yes, it is from Landsat.\nResponse: \"Median value\" refers to the median value of multiple images on the same\npixel.\nbeen reported in this paragraph.\nResponse:Agree and we deleted the repeated content.\ncomparison. Please rephrase.\nResponse:Thanks for your patient correction. We rephrased this sentence, please see\nLine 415.\nResponse:We revised this to “exhibited a high degree of concordance”, please line\n4461.\nthe maps of planted forest.\nResponse:The vegetation type map is a geographic representation that classifies and\nillustrates the various types of vegetation. It contains 17 forest types, primarily\nrepresenting a potential spatial distribution of these types (Zhang 2007). Based on this\nvegetation map and spatial nearest union method, we can divide the planted forest\nmap into different forest types. Then according to the study of Lai et al. (2016), using\ncarbon density, we can estimate carbon storage of planted forests.\nshould be open and public? If not, which “data” will be made available. I think it is\nresponsible act of transparent science to release maps and accounts.\nResponse: The data will be made available following the article's publication.\n\nSupplementary Notes\nwere available before 2003?\nResponse:Please see the response to Comment 37, and see details in line 359-381.\nconfusion matrix listing all classes that were mapped.\nResponse:We have solely classified planted and natural forests on the basis of the\nCLCD forest masks; for other types, we utilized the existing CLCD products. Please\nalso the response to the General comments 2-2).\nResponse:Thanks for the suggestion, we added the study of Zhang et al. (2015) as a\nreference. Please see Supplementary line 46\nResponse:To avoid misunderstanding, we deleted the word of “original”, please see\nline 62.\nResponse:we determined the parameter according to the overall classification\naccuracies, which were calculated using the testing samples. We revised this sentence,\nplease see Supplementary line76-77.\nPlease specify instead the origin of the data\nResponse: Initially, we have to concede that a clerical error occurred; the samples\nwere not derived from stratified random sampling but from systematic sampling,\nwhich we randomly constituted.As the field validation samples utilized were not\nrandomly selected, this may impart bias to the validation results (Olofsson et al.,\n2014); hence, systematic sampling was adopted to enhance the precision verification\nof the data product (Rizayeva et al., 2023),Our initial step was to create a 0.1° grid\ncovering the entire country, followed by the establishment of a central point within\neach grid cell,combining these central points with forest survey maps and high-\nresolution remote sensing imagery from Google Earth (Wang et al. 2021), with forest\ntypes ascertained by experts in vegetation ecology,subsequently utilizing these\nsystematically collected samples to validate these maps.For additional details, consult\nthe Supplementary material, lines 118-131.\nResponse:Thanks for the suggestion. We revised this sentence to “intercomparison\nwith forest inventory map”. Please see line 97-98.\nreferred to here.\nResponse:This is a misquote, we revised, and it should be Supplementary Fig. 14b\nand Fig. 16a. In the revised manuscript, they have been listed as Supplementary Fig.\n4b and 11b.\nconsequence of the higher quality of the Landsat data?\nResponse:We attribute this to two main factors: first, the quality of remote sensing\nimagery. It is well recognized that the quality of Landsat imagery has been\nprogressively improving, which in turn directly impacts the precision of classification\n(Yang and Huang 2021).Second, it pertains to the training and validation samples\nemployed, as our samples span from 2003 to 2020, predominantly amassed post-2015,\nand when we extrapolate the training and validation samples for each period using\nNDVI time series change detection techniques, unavoidable errors in the sample set\nconstruction arise from the inherent inaccuracies of change detection,the earlier the\ntime frame of the sample extrapolation (nearing 1990), the more substantial the\nresultant noise, which ultimately impairs the classification accuracy (Calderón-Loor,\nHadjikakou, and Bryan 2021).\nresolution of 0.1 x 0.1 deg”. Could you please clarify?\nResponse:Please see response to Comment 58.\nResponse: There is a missing reference, the accuracies were shown Supplementary\nFig.3. We added this in Line 138 and 141.\nperiod 1990-2000?\nResponse:According to the statistical year book of China, the planted forest data were\nonly available from 2000 to 2020, and updated every five years.\naddressed as well.\nResponse:Thanks for the suggestion. We re-drawn this figure, as shown in Fig.R6.\nPlease see response to Comment 17.\nResponse:The planted forest map of National Forest Inventory Map was digitized\n(Peng et al. 2014).\nthe figure does not show agreement. It is a comparison of totals. Both maps could be\nwrong in how planted forests are distributed but the totals agree. Any thoughts on\nhow to address this issue?\nResponse:Supplementary Figure 19 illustrates the disparity within a 0.1° grid\nbetween the planted forest area derived from our study and that from the digitized\nNational Forest Inventory Map,which is obtained during 2004 to 2008 conducted by\nState Forestry and Grassland Administration of China. Hence, we compared our\nresults in 2000, 2005 and 2010 with this inventory map because they're close in time.\nAs shown in Fig.R8, panels a-c displays the area differences within 0.1° grids\nbetween the mapped results for 2000, 2005, and 2010 and the National Forest\nInventory Map. Panel d presents the frequency distribution based on the area\ndifference range, with numbers indicating proportions. It can be observed that for the\nyears 2000-2010, the grids with area differences less than 30 square kilometers\naccount for 79.3%, 79.1%, and 72.7%, respectively, indicating good consistency\nbetween the forest map generated in this study and the inventory data. To provide a\nclearer presentation, we have added explanations for panels a-d in the figure captions,\nand please see line 171-174.\nWe acknowledge that there may be errors in both the National Forest Inventory Map\nand the maps we generated. However, the National Forest Inventory Map is currently\nthe most authoritative dataset for representing the distribution of China's planted\nforests. Many scholars have used it to study the ecological benefits of China's planted\nforests, such as Peng et al. (2014), who utilized it to assess the climatic benefits of\nafforestation in China. Additionally, the spatial database of planted forest released by\nthe World Resources Institute also uses this data as a representation of China's planted\nforests (Harris et al., 2019). Therefore, to validate the accuracy of the map produced\nin this study, we are making the assumption that this dataset is accurate for the\npurpose of comparison.\nFig.R8. Comparison of planted forest area within each 0.1° between the resultant\nmaps and available planted forest dataset in 2000, 2010 and 2020. a, b and c are the\nspatial distribution of area difference; d is the frequency statistics of grid counts with\nvarious area differences.\n“medium”) whereas it is high when > 0.4 is misleading. It is generally agreed upon\nthat high accuracy is > 0.7. If this understanding is correct, the planted forest maps are\nhighly uncertain, with some relevant impact on the relevance and the outcome of this\nstudy. Please clarify.\nResponse: The uncertainty of each pixel in the resultant maps was assessed using the\noutputs of the RF classifier. Classification uncertainty for a pixel, labeled \"u,\" is\nconveyed through a probability vector produced by the RF probabilistic classifier\n(Loosvelt et al., 2012). This vector contains probabilities corresponding to the pixel's\nassignment to various classes. The maximum probability in the vector, termed “p ”,\nmax\nindicates the most probable class based on the RF model. The value \"u\", calculated as\n1 - p , gauges the strength of class assignment and the potential for confusion. A\nmax\nhigh u value suggests ambiguity or confusion between classes, while a low u value\nimplies greater model confidence in the assigned class.\nTherefore, the uncertainty <0.4 means that the classification probabilities are greater\nthan 60%, which accounts for over 80% of all classification region (Liu et al., 2004).\nWe recalculated the proportion of the uncertainty less than 0.3, which was 77.78%,\n77.70%, 80.06%, 78.52%, 74.60%, 72.49%, and 65.41%, respectively from 1990 to\n2020. This implies that, except for the year 2020, during all other periods, there is\nover 70% planted forest pixels having a recognition probability exceeding 70%,\nindicating relatively low uncertainty.\nWe updated this results in Supplementary note line 158 to 160.\nResponse: We enlarged page and uploaded the all-high-resolution figures.\nindicated that the slope and the intercept increase with time. Could it be a symptom\nthat the maps suffer from the quality of the Landsat data? In principle, forest patches\nof a certain size should always be mapped at a certain size +/- some uncertainty\nwhereas here we observe a clear temporal trend.\nResponse: Here, we compared the planted forest area mapped in our study with the\nstatistical area at provincial scale referred to (Wang et al., 2020). To present the\ncorrelation between our mapped area and statistical area, we re-drawn this figure, as\nshown in Fig. R6 (Supplementary Fig 3).\nResponse: Here we show the area difference between our generated planted forest\nmaps and forest inventory map with each 0.1-degree grid. Details to see the response\nto Comment 67.\n\nReference\nCalderón-Loor, M., et al. High-resolution wall-to-wall land-cover mapping and land\nchange assessment for Australia from 1985 to 2015, Remote Sens Environ. 252,\n112148 (2021).\nCao, S. Why large-scale afforestation efforts in China have failed to solve the\ndesertification problem. Environ. Sci. Technol. 42, 1826–1831 (2008).\nCao, S., et al. Excessive reliance on afforestation in China's arid and semi-arid\nregions: Lessons in ecological restoration, Earth Sci Rev. 104, 240-45 (2011).\nChen, S., et al. Current and future carbon stocks of natural forests in China, For. Ecol.\nManage. 511: 120137 (2022).\nKhunrattanasiri, W. Application of Remote Sensing Vegetation Indices for Forest\nCover Assessments, in Concepts and Applications of Remote Sensing in\nForestry, M.N. Suratman, Editor. 2022, Springer Nature Singapore: Singapore. p.\n153-166.\nLai, L., et al. Carbon emissions from land-use change and management in China\nbetween 1990 and 2010, Science Advances, 2, e1601063 (2016).\nLi, R., et al. One of report of China's Six Key Forestry Programs: visiting of Sand\nControl Programs for areas in the vicinity of Beijing and Tianjin. Forestry &\nHumans 9, 14–18 (2001) (in Chinese).\nLiu, J., et al. Spatiotemporal characteristics, patterns and causes of land use changes\nin China since the late 1980s, Dili Xuebao/Acta Geogr. Sin., 69, 3–14 (2014).\nLiu, W., et al. Uncertainty and confidence in land cover classification using a hybrid\nclassifier approach. Photogramm Eng Remote Sensing 70(8), 963-971 (2004).\nLoosvelt, L., et al. Random Forests as a tool for estimating uncertainty at pixel-level\nin SAR image classification. Int J Appl Earth Obs Geoinf. 19, 173-184 (2012).\nOlofsson, P., et al. Good practices for estimating area and assessing accuracy of land\nchange. Remote Sens Environ. 148, 42–57 (2014).\nPeng, S., et al. Afforestation in China cools local land surface temperature, Proc. Natl.\nAcad. Sci. U.S.A. 111, 2915-19 (2014).\nRizayeva, A., et al. Large-area, 1964 land cover classifications of Corona spy satellite\nimagery for the Caucasus Mountains. Remote Sens Environ.113343 (2023).\nState Forestry and Grassland Administration of China. Development report for the\nThree-North Shelterbelt System in the past 40 Years: 1978–2018 (2019).\nWang, G., et al. China's forestry reforms. Science 318, 1556–1557 (2007).\nWang, J., et al. Mapping sugarcane plantation dynamics in Guangxi, China, by time\nseries Sentinel-1, Sentinel-2 and Landsat images, Remote Sensing of\nEnvironment, 247, 111951 (2020).\nXu, B., et al. Biomass carbon stocks in China’s forests between 2000 and 2050: A\nprediction based on forest biomass-age relationships, Sci China Life Sci. 53,\n776-83 (2010).\nYang, J. & Huang, X. The 30m annual land cover dataset and its dynamics in China\nfrom 1990 to 2019. Earth Syst. Sci. Data 13, 3907-3925 (2021).\nYang, Q., et al. Mapping high-resolution forest aboveground biomass of China using\nmultisource remote sensing data, GIsci Remote Sens. 60, 2203303 (2023).\nYang, Z., et al. Loss of soil organic carbon following natural forest conversion to\nChinese fir plantation, For. Ecol. Manage. 449, 117476 (2019).\nZhai, D., et al. Lost in transition: Forest transition and natural forest loss in tropical\nChina', Plant Diversity 39, 149-53 (2017).\nZhang, X. et al. GLC_FCS30: global land-cover product with fine classification\nsystem at 30 m using time-series Landsat imagery. Earth Syst. Sci. Data 13,\n2753-2776 (2021).\nZhou, J., et al. Reconstruction of global MODIS NDVI time series: Performance of\nHarmonic ANalysis of Time Series (HANTS). Remote Sens Environ. 163, 217-\n228 (2015).\n\nReviewer #2:\nThis could be a data paper, but it falls short of a research paper targeting carbon stock\nassessment. The author used Landsat TM images and forest samples to generate\nplanted forest maps in China from 1990 to 2020. Despite the fact that I believe the\nplanted forest maps do add new knowledge to our understanding, the focus of this\nstudy - carbon stock assessment - was too simplified to be convincing.\n\nResponse:Thank you for your valuable feedback. Your insightful comments have\nbeen instrumental in enhancing the quality of our work. We have carefully considered\neach of your comments and made substantial revisions to the manuscript. Specially,\nwe have strengthened the overall theme of the article and added information about the\nmethods and results of carbon stock estimation, along with quantifying and analyzing\nthe errors. Below, we provide comprehensive point-by-point responses to address the\nspecific issues raised during the review process.\net al (2016) (note that it is not Li et al, I believe). This inherits the uncertainties from\nthe study (Lai's study used mean values for the same vegetation type compiled from\ndifferent studies). Besides, forest types are not fixed over time. Instead, they evolve\nand change during succession. Using a fixed type to extrapolate the carbon potential\nwill be biased.\n\nResponse: Thank you for your insightful and professional comments. Firstly, we\nsincerely apologize for the error in naming; it was indeed the study conducted by Lai\net al published in 2016. In order to estimate the carbon storage of planted forests\nduring various periods accurately, we utilized National Forest Inventory Data from\n2004-2008 (representative of the year 2005) to derive carbon densities for different\ntypes of planted forests across different times using age-biomass density equations\nproposed by Xu et al (2010). Then, we estimated the planted forest carbon change in\naboveground biomass by comparing storage in patch locations with the same\nvegetation type (Fig. 2), with the specific process as outlined below:\nFirst-Acquisition of planted forest inventory samples for different forest types.\nExtracting planted forest samples from the National Forest Inventory Data based on\nthe forest type annotations. Dividing the planted forest samples into 17 subsets\n(Supplementary Table 5) according to the forest types within vegetation map.\nSecond-Acquisition of planted forest samples across various periods. Obtaining\nplantation inventory samples for different vegetation types corresponding to different\nperiods, based on age information from inventory samples and remote sensing time-\nseries analysis, which entails two distinct scenarios: One scenario pertains to 1990-\n2000, where samples are directly filtered using the age of trees in inventory data;\nfor instance, the 1990 samples are those inventory samples with a tree age exceeding 15\nyears, mirroring the forest age data from 2005 in our records. The second concerns\nsamples from 2010-2020, utilizing time-series change analysis via remote sensing to\nassess the changes in each 2005 sample through to 2010, 2015, and 2020, preserving\nthose unchanged and discarding any that have altered. By employing these\nmethodologies, we acquire inventory samples for each period, inclusive of their tree\nspecies and age details.\nThird-Estimation of carbon density. With the inventory sample collections established\nfor each period, the age-biomass density equation (Eq 1) at the tree species scale\ncrafted by (Xu et al. 2010) is applied to compute the biomass density for each sample\nunder all forest types, and the carbon density value for each sample is determined\nusing the 0.5 biomass-carbon conversion factor suggested for China’s forests by (Ma\net al., 2002). Subsequently, the average carbon density and its standard deviation for\neach type is calculated. It is important to note that this approach might overstate the\ncarbon density of newly established planted forests between 2005-2020, as their ages\nrange from 1-15 years, and the sample ages for 2010, 2015, and 2020 determined by\nthe aforementioned method are all above 5 years. Consequently, for the newly added\nplanted forests in this period, the study adopts the widely used space-for-time\nsubstitution approach(Blois et al. 2013) (Blois et al. 2013), selecting 2005 samples\naged 1-15 years to estimate the carbon density of these new varied types of planted\nforests using the age-biomass equation (Eq 1).\n1\nw\nB = -at\nIn which B is the biomass density (Mg C1/h+a)k, t e is the tree age (a), w, k, and a are\nconstants that were determined by (Xu et al. 2010).\nFourth-Calculation of carbon storage in planted forests. According to forest types of\nvegetation map, we first classify planted forest into 17 forest types based on the\nprinciple of nearest neighbor in geography. Utilizing the carbon densities of these\nvegetation types corresponding to disparate periods as previously established\n(Supplementary Table 5), the carbon storage of Chinese planted forests for these\nperiods is calculated as:\nN\nC = ∑Di ×Areai\ni=1\nin which is the total carbon storage of planted forest in biomass, and\nare the carbon density and area of the planted forest type. is the number of\nC Di Areai\ntypes. st\ni N\nFifth-Validation. In order to demonstrate the reasonableness of the calculated carbon\nstorage in planted forests, we employed publicly accessible field survey samples\ncreated by (Xu et al., 2019) (download from National ecological data center resource\nsharing service platform of http://www.nesdc.org.cn/) and China’s forest biomass data\ngenerated by (Yang et al., 2023) (download from the website of\nhttps://www.3decology.org/2023/08/02/china-forest-agb-map2019/) for result\nvalidation. The comparison results indicated an R2 ranging from 0.61 to 0.68\n(Supplementary Fig. 14).\nWe supplemented the above method in line 486-541. Using these methods, we\nupdated our carbon estimation results, and re-analysis their dynamics, please see line\n142-248.\nWe have to admit that using the same vegetation type can bias the estimates.\nTherefore, we compared the classification results with updated 2020 China vegetation\nmap (Su et al., 2020), which revealed that over 70% grids had the same vegetation\ntypes with the old vegetation type map generated by 1980s to 2000s (Supplementary\nFig. 17). Moreover, we compared carbon storage estimation results using two\ndifferent vegetation maps and found differences of only about 2% grids greater than\n0.1 Tg C (Supplementary Fig. 17). Although the comparative results indicate minimal\ndifferences, it is inevitable that they will have some impact on the outcomes. In our\nrevised manuscript, we have conducted a thorough discussion and analysis regarding\nthe reasons for the errors. Please refer to line 542-614 for more details.\nIndeed, there is a close relationship between tree height and P-PET. However, tree\nheight is determined by site index, and factors such as soil fertility, slope, and aspect\nare important. Moreover, height is also related to the planting density of the trees.\nUsing a simplified relationship of height and P-PET to further extrapolate the carbon\nstock potential is highly unreliable.\n\nResponse: We agree. In consolidating feedback from various reviewers, we recognize\nthat this section indeed bears limited relevance to our study and fails to emphasize the\narticle's main topic. We also agree with the reviewers' remarks on this section. As a\nresult, we have abbreviated this portion, placing increased emphasis in the manuscript\non the changes in carbon storage due to the expansion of planted forests in China.\nParticularly by adding descriptions of carbon storage estimates and their dynamic\ncharacteristics over various periods and regions, and the carbon storage alterations\ninduced by the conversion of diverse land cover types to planted forests. Details to see\nthe response to Comment 1.\ndiscussion and conclusions. For example, in lines 322-325, this is not reasonable as\nthere are no such absolute differences between planted and natural forests. An\ninstance is that human interventions to expedite the succession process via\ncomplementary planting of seedlings, selective cutting, and pruning in natural\nsecondary forests will also help increase carbon stock. Are those forests natural or\nplanted?\n\nResponse: Following the reviewers' suggestions, we have supplemented the methods\nand results of carbon estimation, quantified potential errors, and made revisions and\nimprovements to the entire manuscript, please refer to the responses to Comment 1\nand 2.Additionally, we have included definitions of planted and natural forests in this\nstudy, narrowing the focus of our research; please see lines 371-379.\n\nReference\nLai, L., et al. Carbon emissions from land-use change and management in China\nbetween 1990 and 2010, Science Advances, 2, e1601063 (2016).\nXu, B., et al. Biomass carbon stocks in China’s forests between 2000 and 2050: A\nprediction based on forest biomass-age relationships, Sci China Life Sci. 53,\n776-83 (2010).\nMa Q., et al. Carbon content rate in constructive species of main forest types in\nnorthern China. Journal of Beijing Forestry University 24(5/6), 96-100 (2002) (in\nChinese).\nBlois, J.L., et al., Space can substitute for time in predicting climate-change effects on\nbiodiversity. Proc. Natl. Acad. Sci. U.S.A. 110(23), 9374-9379 (2013).\nXu, L., et al. A dataset of carbon density in Chinese terrestrial ecosystems (2010s).\nChina Scientific Data, 4(1), p. 7 (2019).\nYang, Q., et al., Mapping high-resolution forest aboveground biomass of China using\nmultisource remote sensing data. GIsci Remote Sens. 60(1), p. 2203303 (2023).\nSu, Y., et al. An updated Vegetation Map of China (1:1000000). Sci. Bull. 65(13): p.\n1125-1136 (2020).\n\nReviewer #3:\nComment for authors：\nThis manuscript obtained planted forest maps for each five-year interval from 1990 to\n2020 based on Landsat data and random forest algorithm, and analyzed the sources of\nplanted forest expansion in China. Meanwhile, this study quantified the C storage\nresulting from the expansion of planted forest over the past 30 years, and evaluate the\nbiomass C storage potential of the additional planted forest. Overall, this study is truly\nunprecedented, carrying milestone significance in enhancing our understanding of the\ntemporal and spatial changes within China's planted forests and their pivotal role in\nmitigating global climate change. However, there are some minor issues that need to\nbe solved before publication.\n\nResponse: We greatly appreciate the positive evaluation of our work by the reviewer.\nIn accordance with the reviewer's suggestions, we have made revisions and\nimprovements to the entire manuscript.\n\nMain Comments:\n1、The manuscript mentions that canopy height is a significant factor related to forest\ncarbon storage potential. However, it appears that the author did not take canopy\nheight into account when calculating the carbon storage conversion from other land\ntypes to planted forests. Instead, the author calculated carbon storage solely based on\nthe conversion area. This oversight raises questions about the accuracy of the carbon\nstorage estimate.\n\nResponse: Thank you for your professional suggestions. First, by integrating the\ncomments of the reviewers, we have reduced the content on canopy height predicting\ncarbon storage potential, and enhanced the analysis of carbon storage changes caused\nby the expansion of planted forests in China (Line 142-236 of the Results section).\nSecondly, to accurately measure the changes in carbon storage, we calculated the\ncarbon density for each vegetation type in each period based on forest age, biomass\ndensity equations proposed by Xu et al (2010), National Forest Inventory Data, and\nChina's 1:1,000,000 vegetation map (Zhang, 2007). By incorporating age to quantify\nchanges in carbon density, and ultimately, using Lai et al.'s approach, estimating the\nabove-ground carbon storage and its variations in planted forests over various periods\n(Line 450-505 of the Method section).Simultaneously, we conducted an in-depth\nanalysis of the errors present in carbon storage estimation, providing a more objective\nconclusion for the results of this study (Line 542-614 of the Method section).\n\n2、In light of the points mentioned above, we assert that the results pertaining to P-\nPET in the final section (Lines 202-219) have limited relevance to the overall content.\nTheir removal will not disrupt the paper's logical structure but will enhance its\nconciseness.\n\nResponse: We agree and we delete this section and strengthened the results of carbon\nstorage in planted forest biomass. Please see the revised manuscript in Line 142-248.\n\n3、In the analysis of Land Use and Land Cover (LULC) transfer, the author identifies\nshrubland, grassland, and natural forest as the primary sources of plantation forest.\nWhile the author provides comprehensive explanations and references for shrubland\nand grassland in the discussion, there appears to be a gap in addressing the key issue\nof natural forest loss, which is arguably the most critical forest type to protect. It is\nevident that a more in-depth discussion regarding the reasons behind the loss of\nnatural forests is warranted.\n\nResponse: Following your suggestions, we concluded several reasons about the loss\nof natural forest. The first aspect is policy-driven. Beginning in the 1970s and 1980s,\nChina extensively harvested natural forests to meet national and economic\ndevelopment demands, leading to a severe decline in natural forest quality over\ndecades (Cao et al. 2011).In efforts to rejuvenate natural forest ecosystems, the state\nenhanced their structure via afforestation, resulting in the conversion of a substantial\npart of low-quality natural forest vegetation into artificial forests (Cao et al. 2011).\nSecondly, to fulfill economic requirements like forest products, especially in China's\nsubtropical areas, numerous natural forests have been transformed into fir plantations\n(Yang et al. 2019).Research indicates that in numerous southern regions in China, the\ncultivation of fast-growing and high-yielding economic planted forests has resulted in\nsubstantial natural forest depletion (Zhai et al. 2017). Given revised manuscript\nfocusing on carbon storage changes resulting from planted forest expansion, we have\nopted not to elaborate excessively on land conversion, instead using land conversion\nas a means to elucidate carbon storage variations.Taking into account the integration\nof the reviewers' kind comments and suggestions, this study concentrates on the\ncarbon storage changes associated with the expansion of planted forests. To better\nemphasize the theme, the study does not provide an overly detailed explanation of\nland conversion, but rather uses land conversion to explain the changes in planted\nforest carbon storage.\n\n4、The quality and consistency of the two vegetation-type maps are crucial factors\ninfluencing the reliability of the results concerning carbon storage changes. I have\nconcerns regarding the consistency in classifying vegetation types within these two\nmaps. If different classification systems were employed, it would render them non-comparable.\nTherefore, it is imperative that the author provides more detailed information about these two\nvegetation maps.\n\nResponse: The classification systems of these two vegetation maps are the same.\nMoreover, the new version was updated based on the old one using a massive number\nof ground survey samples; thus, they share the same classification system (Su et al.,\n2020).We also compared the differences in vegetation types corresponding to planted\nforests between the two vegetation maps, which revealed that over 70% grids had the\nsame vegetation types between the old vegetation type map generated by 1980s to\n2000s and new updated vegetation map in 2020 (Supplementary Fig. 17). Moreover,\nwe compared carbon storage estimation results using two different vegetation maps\nand found differences of only about 2% grids greater than 0.1 Tg C (Supplementary\nFig. 17). We added these descriptions in the revised manuscript line 584-614.\nConsequently, we utilized the older version of the vegetation map, combined with\ncarbon density estimates for different periods, to calculate the carbon storage of\nplanted forests in various periods, and analyzed their spatiotemporal variation\ncharacteristics and reasons.\n\nMinor Comments:\n4、Line 290, native forest should be natural forest;\nResponse: Thanks, we deleted this section and focused on the discussion of carbon\nstorage change in planted forest biomass. Also, in order to avoid grammatical errors,\nwe have used a professional polishing agency to polish the full text, and provided a\nproof of polishing.\n\n5、Line 304 underscores should be underscore.\nResponse: Thanks, and we corrected it.We have also checked and polished the full\ntext, and provided proof of polishing\n\n6、The contents of subgraph b, and d of Fig 3 are not corresponding with the title of\nthe subgraph.\nResponse: Thanks, by incorporating the reviewer's feedback, we have removed Figure\n3, and added information about carbon analysis. Please see Fig.3-5.\n\n7、Line 541, it is suggested to add respectively at the end of the sentence to increase\nreadability.\nResponse: Thanks, and we added it, please see Line in 446\n\n8、Line 502, the full name of the FMASK algorithm should be given at the first time.\nResponse: The full name of FMASK was supplemented in Line 390-391.\n\n9、Line 539 to 541, the classification model adapted to each region and period should\nbe provided.\nResponse: We utilized a random forest model for classification, employing consistent\nparameters for the model. The difference lies in the distinct classification features\ninputted. The input feature set for different periods and regions are provided in the\nSupplementary; please see Supplementary Table 5.We have also referenced\nSupplementary Table 5 in the main text; please see line 444.\n\n10、Line 557, work should be works.\nResponse: Thanks, and we corrected it, please see Line in 542\n\n11、Line 587, delete word code.\nResponse: Thanks, and we corrected it, please see Line in 470\n\n12、Line 589, vegetation-type maps should be vegetation-type map.\nResponse: Thanks, and we corrected it, please see Line in 487.\n\nReference\nCao, S., et al. Excessive reliance on afforestation in China's arid and semi-arid regions:\nLessons in ecological restoration, Earth Sci Rev. 104, 240-45 (2011).\nSu, Y., et al. An updated Vegetation Map of China (1:1000000). Sci. Bull. 65(13): p.\n1125-1136 (2020).\nXu, B., et al. Biomass carbon stocks in China’s forests between 2000 and 2050: A\nprediction based on forest biomass-age relationships, Sci China Life Sci. 53,\n776-83 (2010).\nYang, Z., et al. Loss of soil organic carbon following natural forest conversion to\nChinese fir plantation, For. Ecol. Manage. 449, 117476 (2019).\nZhai, D., et al. Lost in transition: Forest transition and natural forest loss in tropical\nChina', Plant Diversity 39, 149-53 (2017).\nZhang, X. S. Vegetation Map of the People's Republic of China (1:1,000,000) and Its\nIllustration Put to Press, Acta Ecologica Sinica (2007)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Dear Authors\nThe manuscript has been revised thoroughly and according to the comments, for which I am very thankful. All explanations make sense, and the manuscript has become much clearer. Accordingly, the results are in a proper context and well understood. The manuscript will therefore make a valid contribution to Nature Communications. Nonetheless, I still have some remarks that I share with the authors in this review. I would appreciate if these could be clarified in a new revision. In particular, the way errors are characterized is not sufficiently addressed yet and validation of the C dynamics is questionable.\nOnce such basic aspects are clarified, the authors may consider whether to address an additional request, which may further enhance the value of this study. Would be of benefit to add information of C dynamics for the same time period for natural forests? I let the authors consider this option.\n\nComments\nLine 32. The 637.2 TgC are added to what?\nLine 44. A reference to the Agenda 2030 is appreciated. Does the Agenda 2030 specifically address planted forests or forests in general?\nLine 45. Since the topic is planted forest, I am wondering whether the “land” spatiotemporal dynamics rather than the “forest” spatiotemporal dynamics should be referred to.\nLine 119. Could you please clarify in brackets what is the size of the pixel?\nLine 120. Please rephrase “positive trends in area increase of planted forests”.\nLines 123-127. My apologies but the sentences on these lines are not clear to me.\nLine 128. “The decline trend in the rate of planted forest area expansion” contains too many directions (trend, rate, expansion). Please rephrase.\nLine 135. Referring to Fig. 1 a, b and c, is the pixel size 10 x 10 km?\nLine 146. What is the meaning of 16.2 TgC?\nLine 154. Fig. 3 shows an increase but how can one distinguish between C accumulated by existing planted forest and newly planted forests?\nLine 163. I believe that it is the “C storage” of planted forests.\nLine 178. “Change” means “difference”, right?\nLine 185. What is the definition of “standard deviation”? Is it precision?\nLine 253. I would replace “of planted forests C storage” with “terrestrial C storage through planted forests”.\nLines 300-302. Could you at least add a reference and possibly some numbers from this “previous research”?\nLine 305. All forests? I understood that Figures 2 and 3 are about planted forests only so the meaning of “total” below in not clear.\nLine 372. I might have missed this, but what is the definition of forest in this study? My understanding is that when seedlings are planted, they do not count as forest yet but that may just be a matter of definition.\nLine 468. Does “pixel” stand for grid cell?\nLines 487-511. I would appreciate a revision of the text and streamline. In particular lines 490-494 are difficult to comprehend.\nLine 526. Does “tree” here stand for “forest”?\nLines 535-541. Looking at the two datasets cited here, I have doubts about the overall validation. The title of the dataset by Xu et al., suggests that the data was collected around the year 2010. The map-based estimates of AGB by Yang et al., are representative of 2019-2021. None of them appear to have a temporal component which makes the validation of the magnitude of the C trajectories questionable. In addition, the Wang et al. dataset appears to be overall underestimating AGB (Fig. 7 therein), which makes even the assessment in Fig. S14 questionable. In addition, Fig. S14 reports TgC which would mean that the comparison is done at an aggregated scale. I would very much appreciate some clarifications here. As such, these assessments do not qualify as validation of the results of this study.\nLines 542-614. The addition of a section on uncertainties is very much appreciated. It is obvious that all details cannot be clarified but the text now is only scratching the surface of a possibly delicate issue: how large are the uncertainties associate with this study. I am missing a quantitative evaluation of the errors (bias and precision), in particular in the validation of the C estimates and the C trends.\nLine 577-578. Why is the uncertainty based on the max probability of classification? Would this not underplay the real uncertainty?\nLine 582. A histogram instead of multiple illustrations very similar to each other would be more illustrative.\nLine 594. Are planted trees of the same species of other trees already growing in the same area?\nLine 597. Where does one see that 70% of the grids have the same vegetation type in Fig. S17?\nLine 599. The caption of Fig. S19 is unclear to understand the text here.", "reviewer_2": "I feel that the manuscript has been greatly improved. However, I think there are still a few points that need to be clarified. The most important one is to clarify the definition of forest. Could you give a more specific definition of the forest? For example, what was the canopy coverage threshold adopted in developing the forest maps? It was also reported that China also included a specific type of shrubland as forest, as well as economic plantations and bamboo. This is essential because it determines the reliability of your comparisons between the forest area derived from the maps and the statistics reported in the national survey.\n\nOther suggestions:\n1. In lines 119-125, suggest adding resample process here. These results were derived from a map resampled from 30*30 m resolution, right? This should be clarified because you previously mentioned that the maps were from Landsat at 30 m resolution (boolean type data), but here you talked about trends. I was confused until I read the content in Line 464. Please provide details here.\n2. Line 515, this is minor but there are species-specific conversion factors. Please check and see if there are differences if species-specific conversion factors used.\n3. In the calculation of the biomass density, I am confused. Did the author use the equations developed by Xu et al or the equations were developed using survey data? Since the authors have substantial samples, it is strange to use the equations developed by Xu et al.\n4. The reliability of the entire manuscript was built on the forest maps developed previously. Did the results of the carbon stock change (e.g. Lines 30-32 in Abstract) include the uncertainties from forest mapping? Did the authors conduct grid-to-grid comparisons at 0.1 degree or at 30m resolution?", "reviewer_3": "Thank you for your diligent consideration of the review comments. Your thorough revisions not only showcase a profound understanding of the research but also reflect a commitment to academic excellence. Your proactive response to the review comments and the extensive modifications made have significantly enhanced the quality of the manuscript, providing readers with more comprehensive and lucid information.\nI genuinely admire the effort you have invested in the revision process, believing that such dedication will undoubtedly garner greater recognition and impact for your research within the academic community.\nOnce again, thank you for your hard work and positive response to the review comments."}, "author_response": "Response to Reviewers\nReviewer #1\nThe manuscript has been revised thoroughly and according to the comments, for which\nI am very thankful. All explanations make sense, and the manuscript has become much\nclearer. Accordingly, the results are in a proper context and well understood. The\nmanuscript will therefore make a valid contribution to Nature Communications.\nNonetheless, I still have some remarks that I share with the authors in this review. I\nwould appreciate if these could be clarified in a new revision. In particular, the way\nerrors are characterized is not sufficiently addressed yet and validation of the C\ndynamics is questionable.\nOnce such basic aspects are clarified, the authors may consider whether to address an\nadditional request, which may further enhance the value of this study. Would be of\nbenefit to add information of C dynamics for the same time period for natural forests?\nI let the authors consider this option.\n\nResponse: We are grateful for your thorough review and constructive feedback on our\nmanuscript. We are pleased to hear that the revisions have enhanced the clarity and\ncontext of our study and that the results are now well understood. We appreciate your\nacknowledgment of the potential contribution of our work to Nature Communications.\nRegarding your remarks, we agree that further clarification is necessary regarding the\ncharacterization of errors and the validation of carbon dynamics. Firstly, to quantify\nuncertainty, we referred to the method used by Chazdon et al. (2016) and calculated\nthe mean and standard deviation of the carbon density of each forest type based on the\nforest inventory samples. The mean value was used to estimate carbon storage, and\nthen we calculated the total uncertainty by calculating the carbon storage in planted\nforests using the lower and upper standard deviation values to provide a measure of\nuncertainty associated with the total carbon storage of planted forests. We added a\ndetailed explanation about the measurement of uncertainty in Lines 615-620.\nSecondly, to further validate the carbon estimation results, this study once again\ncompared the estimated carbon storage for 2020 with the aboveground biomass data\nreleased by the European Space Agency (Please see Lines 535-544).\nAbout the suggestion on C dynamics of natural forests, we firstly thank you for your\nvaluable suggestions, and we agree with your perspective. However, the main focus\nof this study is to address the carbon dynamics of plantation forests. Therefore, to\nmaintain thematic consistency and focus, we did not address issues related to natural\nforests. In future research, we will prioritize your suggestion to assess the carbon\ndynamics of natural forests. Thank you again for your professional and insightful\nsuggestions.\n\nComments\nResponse: This means that the expansion of planted forest area from 1990 to 2020\nresulted in an increase of 637.2 TgC in the carbon storage of aboveground biomass. We\nadded an explanation in Lines 32-33.\nspecifically address planted forests or forests in general?\nResponse: Thanks for the suggestion, we added a reference to the Agenda 2030. Please\nsee Line 44. The Agenda 2030 addressed forests.\nspatiotemporal dynamics rather than the “forest” spatiotemporal dynamics should be\nreferred to.\nResponse: We agree with your comment, and replaced “forest” using “land”. Please see\nLine 46.\nResponse: We supplemented the explanation about the size of the pixel, please see Lines\n119-121.\nResponse: We simplified the sentence while keeping the original meaning unchanged\n“We found that 91% of the changed pixels from 1990 to 2020 showed positive trends”.\nPlease see Line 121-122.\nResponse: Here, we analyzed the trend of the rate of change in planted forest areas. We\nrephrased this sentence, please see Lines 124-128.\nmany directions (trend, rate, expansion). Please rephrase.\nResponse: We rephrased this sentence, please see Lines 124-128.\nResponse: The size of the pixel is 0.1°. We added this explanation in the caption of\nFigure 1.\nResponse: This represents the standard deviation of carbon estimation. To avoid\nmisunderstanding we added an explanation in Line 146.\naccumulated by existing planted forest and newly planted forests?\nResponse: Thank you for your comment. We constructed two carbon density databases\n(Supplementary Table 6) based on the relationship between forest age and biomass\ndensity in the forest inventory data, using a spatial substitution for time method (Please\nsee details in Methods Lines 488-5424). This includes the carbon density of newly\nplanted forests and stable forests at different time periods. Based on this carbon density\ndatabase, we estimated the carbon storage brought by newly planted forests.\nResponse: Agree, we revised this sentence. Please see Lines 163-164\nResponse: We unified it to changes. Please see Line 178\nResponse: The standard deviation here is derived from multiple carbon density\ncalculations corresponding to each forest type. To quantify uncertainty, we referred to\nthe method used by Chazdon et al. (2016) and calculated the mean and standard\ndeviation of the carbon density of each forest type based on the forest inventory samples.\nThe mean value was used to estimate carbon storage, and then we calculated the total\nuncertainty by calculating the carbon storage in planted forests using the lower and\nupper standard deviation values to provide a measure of uncertainty associated with the\ntotal carbon storage of planted forests. We added a detailed explanation about the\nmeasurement of uncertainty in Line 615-620.\nthrough planted forests”.\nResponse: Thanks for the suggestion. We agreed and replaced using “terrestrial C\nstorage through planted forests”. Please see Lines 253-254\nthis “previous research”?\nResponse: We added references. Please see Line 302.\nonly so the meaning of “total” below in not clear.\nResponse: We corrected this sentence. It is “China’s planted forests”. Please see Line\n306.\nMy understanding is that when seedlings are planted, they do not count as forest yet but\nthat may just be a matter of definition.\nResponse: Thank you for your valuable suggestion. In this study, forests are defined as\nareas with tree height exceeding 5 meters and canopy cover exceeding 15% within a\n30-meter resolution pixel. This definition is inherited from CLCD land cover data, as\nthe plantation forests in this study are extracted within the forest cover range of the\nCLCD time series. We provided detailed explanations about the forest definition in the\nmanuscript. Please see Lines 373-374.\nResponse: Yes. To clarify more clearly, we replaced “pixel” with “grid”. Please see Line\n470.\nlines 490-494 are difficult to comprehend.\nResponse: Thanks for the comment. We re-write this section and tried our best to make\nit more clearly. Please check in lines 489-496.\nResponse: Yes, we revised using “forest”. Please Line 524.\noverall validation. The title of the dataset by Xu et al., suggests that the data was\ncollected around the year 2010. The map-based estimates of AGB by Yang et al., are\nrepresentative of 2019-2021. None of them appear to have a temporal component which\nmakes the validation of the magnitude of the C trajectories questionable. In addition,\nthe Wang et al. dataset appears to be overall underestimating AGB (Fig. 7 therein),\nwhich makes even the assessment in Fig. S14 questionable. In addition, Fig. S14 reports\nTgC which would mean that the comparison is done at an aggregated scale. I would\nvery much appreciate some clarifications here. As such, these assessments do not\nqualify as validation of the results of this study.\nResponse: Thank you for your professional comments. We agree with your point that it\nis indeed difficult to obtain field survey data that truly represents a specific time, and\nthere is a lack of time-series samples for validation. Therefore, in the validation phase,\nthis study only validated the results estimated for 2010 and 2020. Firstly, we verified\nthe carbon storage results of plantation forests in 2010 using field survey data published\naround 2010 by Xu et al. and verified the results for 2020 using aboveground biomass\ndata produced by Yang et al. for 2019. Additionally, to make the validation robust, we\nagain utilized the 2020 global aboveground biomass data product released by the\nEuropean Space Agency. The validation results showed significant consistency between\nour study results and the European Space Agency's data product, with an R² of 0.5.\nOverall, the accuracy of carbon storage estimation in this study ranged from 0.5 to 0.7.\nWe provided further supplementation and explanations on the validation process in the\nmanuscript Line 535-544.\nIn addition, we added an illustration that the comparison is done at an aggregated scale\nin the caption of Fig S14.\nIt is obvious that all details cannot be clarified but the text now is only scratching the\nsurface of a possibly delicate issue: how large are the uncertainties associate with this\nstudy. I am missing a quantitative evaluation of the errors (bias and precision),\nin particular in the validation of the C estimates and the C trends.\nResponse: Thanks for the comment. Please see the response to Comment 13\nWould this not underplay the real uncertainty?\nResponse: Thanks for your professional comment. Random forests, as an ensemble\nlearning method, improve model accuracy and robustness by constructing multiple\ndecision trees and aggregating their predictions (Breiman, 2001). The final\nclassification result is typically based on the \"majority vote\" principle, where the\nclassification supported by the most decision trees is chosen (Breiman, 2001).\nTherefore, the maximum probability, which is the proportion of decision trees that\nselect a certain classification as the final result, is often used to quantify uncertainty in\nthe classification of remote sensing (Pal, 2005). For example, Loosvelt et al. (2012)\nused this method to assess the uncertainty of random forest classification. Therefore,\nwe selected this method to quantify the uncertainty of classification.\nHowever, the method is not without its limitations, which could lead to underestimating\nthe true uncertainty in some scenarios. For example, in cases of class imbalance, where\nsome classes dominate the dataset, the maximum probability might bias towards these\nclasses, even for samples where the model's true certainty is lower (Cutler et al., 2007).\nAdditionally, samples lying close to decision boundaries between classes might exhibit\nhigh maximum probabilities, masking the inherent uncertainty due to their proximity\nto multiple class regions.\nIn the future, supplementary measures can be employed to provide a more nuanced\nview of uncertainty. For example, considering the entropy across all class probabilities\ncan offer insights into the overall uncertainty level, with higher entropy indicating\ngreater uncertainty. Similarly, examining the distribution of votes among classes,\nrather than focusing solely on the majority class, can reveal cases where the model is almost\nequally split between two or more classes, indicating higher uncertainty (Rokach, 2016).\nwould be more illustrative.\nResponse: Thanks for the suggestion. We added a histogram figure in Supplementary\nFigure 16 as shown below:\nsame area?\nResponse: It's similar, and here we refer to the study by Yu et al. (2020).\nin Fig. S17?\nResponse: Sorry, this is a clerical error, it should be over 80%. We calculated the carbon\nstorage in each grid and compared their differences to judge whether the vegetation\ntype within the grid has changed. Because the area of plantation forests is the same in\neach grid if the vegetation types are different, their carbon densities will also differ,\nleading to differences in carbon storage between them; if there is no difference, it\nindicates that the vegetation type has not changed. We counted the number of carbon\nstorage differences equal to 0, which was approximately 80.3%, and annotated it in the\nlegend.\nResponse:The reference here is to Supplementary Figure 17, and we have modified the\nexpression in the original text. Here, we want to illustrate that only 2.1% of the\nestimated differences in carbon storage brought by the two vegetation maps exceed 0.1\nTg. Please see Lines 598-601.\n\nReference\n1. Loosvelt, L., Peters, J., Skriver, H., Lievens, H., Van Coillie, F. M. B., Baets, B. D.,\nand Verhoest, N. E. C., 2012. Random Forests as a tool for estimating uncertainty\nat pixel-level in SAR image classification, International Journal of Applied Earth\nObservation and Geoinformation. 19, 173-184.\n2. Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., &\nLawler, J. J. (2007). Random forests for classification in ecology. Ecology, 88(11),\n2783-2792.\n3. Rokach, L. (2016). Decision Forest: Twenty Years of Research. Information Fusion,\n27, 111-125.\n4. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.\n5. Pal, M. (2005). Random forest classifier for remote sensing classification.\nInternational Journal of Remote Sensing, 26(1), 217-222.\n6. Transforming our world: the 2030 Agenda for Sustainable Development (United\nNations, 2015); https://go.nature.com/3CsPxqn\n7. Chazdon, R. L., Broadbent, E. N., Rozendaal, D. M. A., Bongers, F., Zambrano, A.\nM. A., Aide, T. M., Balvanera, P., Becknell, J. M., Boukili, V., Brancalion, P. H. S.,\nCraven, D., Almeida-Cortez, J. S., Cabral, G. A. L., de Jong, B., Denslow, J. S.,\nDent, D. H., DeWalt, S. J., Dupuy, J. M., Durán, S. M. et al. (2016). Carbon\nsequestration potential of second-growth forest regeneration in the Latin American\ntropics. Science Advances, 2(5), e1501639.\n\nReviewer #2:\nI feel that the manuscript has been greatly improved. However, I think there are still a\nfew points that need to be clarified. The most important one is to clarify the definition\nof forest. Could you give a more specific definition of the forest? For example, what\nwas the canopy coverage threshold adopted in developing the forest maps? It was also\nreported that China also included a specific type of shrubland as forest, as well as\neconomic plantations and bamboo. This is essential because it determines the reliability\nof your comparisons between the forest area derived from the maps and the statistics\nreported in the national survey.\n\nResponse: Thank you for your valuable suggestion. In this study, forests are defined as\nareas with tree height exceeding 5 meters and canopy cover exceeding 15% within a\n30-meter resolution pixel. This definition is inherited from CLCD land cover data, as\nthe plantation forests in this study are extracted within the forest cover range of the\nCLCD time series. We provided detailed explanations about the forest definition in the\nmanuscript. Please see Lines 373-374.\n\nOther suggestions:\n1. In lines 119-125, suggest adding resample process here. These results were derived\nfrom a map resampled from 30*30 m resolution, right? This should be clarified because\nyou previously mentioned that the maps were from Landsat at 30 m resolution (boolean\ntype data), but here you talked about trends. I was confused until I read the content in\nLine 464. Please provide details here.\n\nResponse: Thanks a lot for the suggestion, we added the description of the resampling\nprocess. Please check in lines 119 -121.\n\n2. Line 515, this is minor but there are species-specific conversion factors. Please check\nand see if there are differences if species-specific conversion factors used.\n\nResponse: We thank you and agree with your comment. This study mainly referred to\nthe recommendations provided by Fang et al (2021). in their book “Carbon Budgets of\nForest Ecosystems in China”, and used a conversion coefficient of 0.5 more suitable\nfor Chinese forest ecosystems. According to Chen (2003), the average carbon content\nof tree species in China is greater than 0.45. Using 0.45 as the conversion coefficient\nwould result in an underestimation of the biomass carbon pool. Therefore, using 0.5 as\nthe conversion coefficient for forest biomass carbon may be more reasonable.\n\n3. In the calculation of the biomass density, I am confused. Did the author use the\nequations developed by Xu et al or the equations were developed using survey data?\nSince the authors have substantial samples, it is strange to use the equations developed\nby Xu et al.\n\nResponse: Thanks for your valuable comment. We directly used the models established\nby Xu et al. because the data we used to establish carbon density were the same as theirs.\n\n4. The reliability of the entire manuscript was built on the forest maps developed\npreviously. Did the results of the carbon stock change (e.g. Lines 30-32 in Abstract)\ninclude the uncertainties from forest mapping? Did the authors conduct grid-to-grid\ncomparisons at 0.1 degree or at 30m resolution?\n\nResponse: Thanks for your insightful comment. We agree with your point. This study\nvalidated the accuracy of the plantation forest map through various commonly used\nmethods, with an overall accuracy ranging from 77.3% to 81.8% and good consistency\nwith statistical data, with an R² between 0.8 and 0.9, indicating the reliability of the data\nproducts obtained in this study. Therefore, based on such results, we conducted carbon\nstorage estimation research. In the process of carbon storage estimation, we did not\nquantify the uncertainty of the planted forest maps but mainly considered the influence\nof carbon density. However, it is undeniable that the accuracy of plantation forest\nmapping does affect the estimation of carbon storage. Based on your valuable\nsuggestion, we will consider further quantifying the uncertainty of plantation forests in\nfuture research.\nThe comparisons and analyses in this study were performed within 0.1-degree grid cells.\n\nReference\n1. Chen X. 2003. Study on the carbon sink function of major forest types in North\nChina. Beijing Forestry University (in Chinese).\n2. Fang J., Zhu J., Li P. 2021. Carbon Budgets of Forest Ecosystems in China. Science\nPress (in Chinese).\n\nReviewer #3:\nThank you for your diligent consideration of the review comments. Your thorough\nrevisions not only showcase a profound understanding of the research but also reflect a\ncommitment to academic excellence. Your proactive response to the review comments\nand the extensive modifications made have significantly enhanced the quality of the\nmanuscript, providing readers with more comprehensive and lucid information.\nI genuinely admire the effort you have invested in the revision process, believing that\nsuch dedication will undoubtedly garner greater recognition and impact for your\nresearch within the academic community.\nOnce again, thank you for your hard work and positive response to the review\ncomments.\n\nResponse: Thank you for your positive feedback on our revised manuscript. I look\nforward to my research receiving more attention from our peers and contributing\nvaluable insights to our field. Thank you again!"}]

Decision letter
REVIEWERS' COMMENTS
Reviewer #1 (Remarks to the Author):
The manuscript underwent a second through revision and all my questions have been
addressed. Looking forward to seeing the article published.