Estimating forest aboveground carbon sink based on Landsat time-series and its response to climate change

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Taking Pinus densata in Shangri-La as the research object, we established three Random Forest (RF) dynamic models based on Landsat time series and ground data with 5-year interval variation, 10-year interval variation, and annual average variation. Then, Genetic Algorithm (GA) was applied to optimize the parameters of RF to establish GA-RF dynamic models, and selected the optimal model to estimate the carbon sink intensity (CSI) of Pinus densata . Finally, climate-driven mechanisms were explored by correlation analysis. We found that 1) the GA-RF model based on the annual average variation had the highest accuracy with an R 2 of 0.83. 2) The CSI of Pinus densata in Shangri-La was 7.84–12.35×10 4 t C·hm − 2 from 1987 to 2017. 3) Precipitation had the greatest effect on CSI. The joint weak drive of CSI by precipitation, temperature and surface solar radiation was the most dominant form of CSI drive for Pinus densata . These results suggest that the GA-RF model can be used for large-scale long-term estimation of above-ground carbon sinks in highland forests. In addition, the precipitation-led multifactorial synergistic driving mechanism will stabilize the carbon sink capacity of Pinus densata in the long term. Forest aboveground carbon sink Time series Dynamic modelling Genetic Algorithm Climate change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Forest, as a major component of terrestrial ecosystem, possess significant carbon sink potential 1 . Forest carbon sink refers to the process by which green plants absorb CO 2 from the atmosphere through photosynthesis and sequester it within the forest, playing a crucial role in global climate change mitigation 2 . Forest carbon sink accounts for 76–98% of terrestrial carbon sink (CS), holding an important position in the global carbon sink landscape 3 , 4 . In recent years, forest carbon sink have garnered widespread attention due to their substantial economic and ecological value, becoming a vital strategy for addressing climate change and achieving carbon neutrality 5 . Effective monitoring and assessment of forest carbon sink capacity are essential for maintaining the carbon balance of terrestrial ecosystems, achieving efficient management of forest resources and mitigating climate warming 6 . The estimation of forest carbon sink can be achieved through quantitative analysis of variations in forest carbon stock over specific periods 7 . Remote sensing-based estimation is one of the best methods for large-scale and long-term studies 8 . Since the launch of the first Landsat satellite in 1972, its long lifespan and good data continuity have made it an ideal choice for time series research 9 . Machine learning algorithms are important tools for estimating CS based on remote sensing images, widely used in assessing forest resources such as aboveground biomass (AGB) and carbon stock 10 . Models established by Random Forest (RF) method demonstrate higher stability and accuracy 11 , 12 , effectively avoiding the underestimation of CS seen in some ecosystem process models 13 . Parameter adjustment is a common strategy for improving machine learning-based models 14 . Previous studies have shown that optimizing parameter selection by genetic algorithms (GA) is also an effective method to enhance model accuracy 15 , 16 . However, not every algorithm can be adapted to the model. A comparative analysis is needed to determine whether the optimization of the algorithm meets the experimental requirements. Dynamic models are increasingly gaining attention in the field of forest remote sensing monitoring due to their strong data correlation over long time series 17 . Researches indicated that dynamic models perform better in estimating AGB 18 , 19 . Meanwhile, it allows for evaluation of time-dependent phenomena and period needed for recovery 20 . While steady-state models remain the foundation for forest resource estimation, dynamic models provide researchers with new perspectives. Currently, studies applying dynamic models to forest carbon sink estimation are relatively rare, making this attempt highly valuable. Given that forest carbon sink is dynamic variable, we established a dynamic model based on this theory to accurately analyze the long-term changes in forest carbon sink. Global warming has become an undeniable fact, and with the intensification of climate change, the capacity of terrestrial CS will be significantly affected 21 . China's terrestrial CS is particularly sensitive to climate change, forest carbon sink is even more significantly influenced by climate change 22 . Long-term and severe climate changes may lead to a weakening of forest carbon sink. Lack of precipitation and prolonged droughts can reduce the carbon absorption capacity of large forests such as the African rainforest and the Amazon rainforest 23 , with some regions potentially shifting to being carbon sources 24 . Quantifying the impact of climate factors on forest carbon sink is crucial for ecological protection and sustainable development. However, accurately quantifying the spatial and temporal effects of climate factors on forest carbon sink remains a major challenge in current research. Shangri-La is a typical ecological carbon sink in China's Yunnan Province, and its geomorphologic and climatic characteristics are highly representative of southwestern China (Fig. 1 ). Pinus densata is one of the dominant tree species in Shangri-La. In addition to Yunnan, Pinus densata is also widely distributed in Sichuan and Tibet, and plays an important role in the carbon cycle in southwest China. Shangri-la is located in the southwest of the Qinghai-Tibet Plateau, with complex terrain, changeable climate and fragile ecological environment, so it is difficult to accurately estimate forest carbon sink intensity (CSI). Besides, the climate driving mechanism of carbon sink of Pinus densata is still unclear. Therefore, in this study, remote sensing-based dynamic models were used to estimate the carbon sink capacity of Pinus densata in Shangri-La, and correlation analysis methods were used to explore the climate driving mechanism (Fig. 2 ). The main objectives are the following: (1) establishing carbon sink dynamic models based on three different types of variation; (2) analyzing the effect of GA on RF dynamic models; (3) estimating the carbon sink of Pinus densata in Shangri-La and analyze its temporal and spatial changes; (4) exploring the climate driving mechanism. Results Analysis of modelling For each of the three types of variation, we selected 10 of the strongest correlated remote sensing factors, all of which exhibited highly significant correlations ( P < 0.01) suitable for modeling (Table 1 , Fig. 3 ). The optimal modelling factors with strong and highly significant correlations among the three types of variation were all texture feature factors, among which SK and SM appeared most frequently. Table 1 Screening factor for each type of variation. Where the naming form of the texture factor is RXBYFF , where R is the texture window, X is the size of the window, and the value of X is an odd number within 1 ~ 19 (including 1 and 19); BY is a certain single band of the image, and the value of Y is 1 ~ 5 and 7; FF is the abbreviation of texture factor. For example, R9B4SK is the SK of the fourth band under the 9×9 window. Variation type Remote sensing factors 5-year variation R9B4SK、R7B4SK、R9B4SM、 R3B3SK、R3B2SK、R17B1SM、 R3B4SK、R19B1SM、R15B1SM、R7B4CC 10-year variation R11B1SM、R7B1SM、R9B1SM、 R15B7SM、R17B7SM、R17B1SM、 R19B7SM、R19B1SM、R15B1SM、R7B2CC Annual average variation R9B4SK、R7B4SK、R9B4SM、 R3B3SK、R3B2SK、R17B1SM、 R3B4SK、R19B1SM、R15B1SM、R13B1SM The optimal parameters of each model of the RF established by the three variations respectively are shown in the Table 2 . The optimal parameters of the GA-RF models are shown in Table 3 . GA-RF models were optimized to spend exponentially more time on training than RF models. In addition, all the six dynamic models ranked the input factors through their importance and output the modelling contribution values of each factor (Fig. 4 ). Table 2 Optimum parameters of RF Variation type n_estimators max_depth min_samples_leaf min_samples_split 5-year variation 90 10 1 1 10-year variation 40 10 2 1 Annual average variation 170 10 1 1 Table 3 Optimum parameters of GA-RF Variation type n_estimators max_depth min_samples_leaf min_samples_split 5-year variation 80 20 2 1 10-year variation 60 20 2 1 Annual average variation 70 10 2 1 The accuracy of the six dynamic models is shown in Fig. 4 . Among the dynamic models with each type of variation, the R 2 and P of the GA-RF model are greater than that of the RF model, and the RMSE is smaller than that of the RF. Combining the evaluation metrics, it can be found that the GA-RF fit based on the annual average variation is the best, which has improved the R 2 , lowered the RMSE, and improved the P compared with the optimal conventional RF model. This shows that the accuracy of the RF-based dynamic model is improved after GA optimization. As shown in Fig. 6 , R 2 , rRMSE and P of GA-RF model based on annual mean change is better than that of other types of GA-RF model. Due to the different time scales, the RMSE should be standardized to the same scale when making comparisons 25 . After multiplying the RMSE of the 5-year variation by 2 and the annual average variation by 10, we found that GA-RF based on the annual average change possesses the smallest RMSE. Thus the GA-RF model accuracy of the annual average variation is better than that of the models with the 5-year and 10-year variation, which is the optimal dynamic model. Estimation of CSI From 1987 to 2017, we divided the period into six intervals of five years each. According to the model evaluation results, the GA-RF model based on annual average variation was used to estimate CSI. By integrating the data on Pinus densata distribution, we derived the spatial distribution map of CSI of Pinus densata in Shangri-La (Fig. 5 ). The CSIs for periods 1–6, respectively, were 10.53×10 4 t C·hm − 2 , 12.35×10 4 t C·hm − 2 , 10.17×10 4 t C·hm − 2 , 11.75×10 4 t C·hm − 2 . 7.84×10 4 t C·hm − 2 and 10.2×10 4 t C·hm − 2 . From 1987 to 2017, CSI of Pinus densata in Shangri-La fluctuated between 7.84 and 12.35×10 4 t C·hm − 2 , with an overall average CSI of 10.48×10 4 t C·hm − 2 . There was a general declining trend, decreasing from 10.53×10 4 t C·hm − 2 in Period 1 to 10.2×10 4 t C·hm − 2 ·a − 1 in Period 6. The period with the strongest carbon sink capacity was Period 2, with an CSI of 12.35×10 4 t C·hm − 2 , while the weakest period was Period 5, with an CSI of 7.84×10 4 t C·hm − 2 . However, compared to the end of the last century, the overall CSI of Pinus densata in Shangri-La has tended to stabilize, showing little variation. Significant differences in CSI of Pinus densata are observed across various townships in Shangri-La, reflecting different carbon sink capacities (Fig. 6 a). Geza has the highest CSI, ranging from 1.78–3.36 × 10 4 t C·hm⁻ 2 . Jiantang follows, with a CSI of 1.69–2.54 × 10 4 t C·hm⁻ 2 . Both townships have average CSI greater than 2×10 4 t C·hm − 2 , classifying them as high CSI areas. In contrast, the average CSI of Pinus densata in the townships of Dongwang, Luoji, and Nixi fall between 1×10 4 t C·hm − 2 and 2×10 4 t C·hm − 2 , classifying them as moderate CSI areas. The remaining townships have CSI below 1×10 4 t C·hm − 2 and do not reach the average CSI across various periods, classifying them as low CSI areas. The CSI of Pinus densata in each township fluctuated during 1987–2017 (Fig. 6 b). Geza and Jiantang show significant variations in CSI but maintain high CSI over the years. Conversely, although Hutiaoxia, Xiaozhongdian, and Jinjiang had weaker CSI, they exhibited little variation during the study period, resulting in stable carbon sink capacities. Analysis of climate driving force Pixel level analysis of the correlation of CSI of Pinus densata with MAP, MAT and SSR temporal changes from 1987 to 2017 based on the partial correlation coefficient and T-test (Fig. 7 , 8 ). The mean value of the partial correlation coefficient between MAP and CSI was 0.018. The area with positive correlation accounted for 51.41% of the total area. The positively correlated areas accounted for 35.65% of the area and were distributed throughout Shangri-La, mainly in the central region. The negatively correlated areas accounted for 48.59% of the total area, and the significantly negatively correlated areas accounted for 32.84% of the total area, which were distributed in the southern part of the study area with low CSI. The mean value of the partial correlation coefficient between MAT and CSI was − 0.015, with 48.24% of the area positively correlated, and 32.62% of the area significantly positively correlated. The proportion of negatively correlated areas was 51.76%, and the proportion of significantly negatively correlated areas was 36.31%, and both positively and negatively correlated areas were distributed throughout the whole territory, without being concentrated in a particular region. The mean value of the partial correlation coefficient between SSR and CSI was − 0.013. The positive correlation areas accounted for 45.09% of the total area, of which the significant positive correlation areas accounted for 24.77%, mainly in the central part of the study area. The area of negatively correlated areas accounted for 54.91%, of which 31.75% were significantly negatively correlated, mostly in the northern part of the study area. The mean multi-correlation coefficient between CSI and climate factors in Shangri-La is 0.65 ( P < 0.05), showing that the comprehensive impact of climate change significantly promotes the growth of CSI of Pinus densata in this area. There are no notable spatial differences in the overall correlation between CSI and climatic factors. Regions with highly significant positive correlations account for 35.36% of the total area of the study region and are widely distributed throughout the area (Fig. 9 ). In Shangri-La, 96.42% of the variation in CSI of Pinus densata is driven by climatic factors (Fig. 10 ). Among these, the areas jointly influenced by MAP, MAT and SSR account for the largest proportion, reaching 26.91%, and are widely distributed throughout the study area. In the single-factor driver, the proportion of areas driven by MAP only is 21%, significantly higher than those influenced by MAT and SSR. Specifically, areas driven by MAP only are primarily located in the northern and northeastern Shangri-La; areas driven by MAT only are mainly concentrated in the central; while areas influenced by SSR only are distributed across the central and southern Shangri-La. The areas influenced by other types of climatic drivers on CSI account for less than 10% and are dispersed within the study area. In summary, it is evident that among all driving factors, whether acting independently or in conjunction with others, MAP has the most significant impact on CSI. This indicates that precipitation is the primary driving factor affecting the CSI of Pinus densata . Furthermore, the area influenced by multiple factors exceeds that influenced by single factors, suggesting that climate factors primarily exert their effects on the CSI of Pinus densata through synergistic interactions. Discussion Accurately estimating forest carbon sink has long been a contentious challenge in current research. Studies conducted in the same region often yield differing estimates of carbon sink capacity 4 . Due to the unique temporal and spatial characteristics of plateau and variations in estimation methods, there is considerable uncertainty in the accurate estimation of forest carbon sink in Shangri-La 26 . The complex topography of the plateau and various influencing factors on vegetation growth, including the location of regenerated forests, differences in forest age, and the rate of forest renewal, contribute to this uncertainty 27 . These factors lead to significant discrepancies in estimates. Furthermore, long-term soil variability in the Tibetan Plateau can affect the growth of scattered conifers, resulting in quantifiable uncertainties regarding initial soil conditions, which may further impact carbon sink estimations 28 . Therefore, the processing of sample plots is a very important step in the modeling 25 . We eliminated sample plots with too large or too small AGBs at the beginning to ensure that there were no young forests and no sample plots with too high densities. Subsequently, the Pauta criterion was used to remove anomalous data and reduce uncertainty. Currently, machine learning models have become important vehicles for remote sensing estimation methods. However, using machine learning models for estimation also has its limitations, such as parameter tuning, high data requirements, simulation accuracy, and model stability 10 . Additionally, the selection of model parameters and the timing of data collection are critical factors. While machine learning models can achieve good accuracy after parameter tuning, this state is not entirely stable and often requires multiple iterations of parameter adjustments 29 . Furthermore, varying data sources and data collected at different times can lead to significantly different results from the same model 13 . This raises higher demands for satellite data resolution, the sources of plot data, and the accuracy of the data. We chose three types of time interval variables to compare the effects of long, medium, and short time intervals on model accuracy. The results demonstrate that shorter time intervals minimize uncertainty in time series studies. Differences in estimation methods can lead to even greater uncertainties. Various estimation methods are influenced by factors such as the number and distribution of parameters, observation stations or plots, and scale conversion. These issues are particularly pronounced in plateau, resulting in significant disparities in CS estimates, which can exceed a factor of ten 30 . For instance, Wang et al. estimated China's terrestrial CS using atmospheric inversion methods, yielding a result of 1.11 ± 0.38 Pg C·a⁻¹, which accounts for approximately 60% of the global CS during the same period—substantially higher than estimates from other researchers at that time 31 . In contrast, Martin et al. employed the eddy covariance method, integrating machine learning techniques with carbon flux data obtained from the Global Flux Observatory Network (FLUXNET), to estimate the global Net Ecosystem Production (NEP) at 23 Pg C·a⁻¹, approximately eight times the global terrestrial CS 32 . In this study, the accuracy of the classic RF model is greatly improved by introducing GA for hyperparameter optimization. A number of evaluation indexes showed that the accuracy of the final model met the estimation requirements. The rationality of the model was further ensured based on the selection of the type of variation. Moreover, models are unable to account for the relatively frequent natural disturbances in forests and recent reconstructions, and the datasets may not capture recovery areas following crop rotation or small-scale logging 33 . Therefore, reducing estimation uncertainty and exploring new methods with higher accuracy will continue to be the focus of forest carbon sink research in the future. Extreme climatic conditions pose the most significant threat to forest carbon sink 34 . Drought has caused irreversible carbon losses in numerous large forest ecosystems worldwide 23 . We found that the CSI of Pinus densata in Shangri-La decreased in both the 1997–2002 and 2007–2012 periods. This is due to Shangri-La experienced severe drought impacts from 1997 to 2002 and again from 2007 to 2012, with precipitation levels lower than historical averages and temperatures exceeding historical norms. Notably, in 2010, a historic drought occurred in Yunnan Province 35 , resulting in a substantial decline in precipitation in Shangri-La and a peak in temperatures during the 2007–2012 period. This contributed to the lowest CSI of Pinus densata during that time. Although there are varying degrees of correlation between individual climate factors and the CSI of Pinus densata in Shangri-La, the influence of climate factors on CSI is often not due to isolated effects but rather the result of synergistic interactions among multiple factors during actual processes. Several scholars 13 , 27 have analyzed the driving factors of CS in global terrestrial ecosystem by integrating multiple climate factors and found that most climate factors exhibit synergistic effects, which are significantly influenced by precipitation. This finding aligns with the conclusions of this study. Additionally, in China, the influence of climate factors on forest carbon sink varies regionally 36 . For instance, in Southeast China, precipitation has less impact on forest carbon sink compared to temperature 37 , while in Northwest China, forest carbon sink show a significant positive correlation in areas of both high and low precipitation, but a significant negative correlation in areas of moderate precipitation 38 . Moreover, we found that the CSI of Pinus densata is greater in low-temperature environments, which aligns with the observation that photosynthetic rates are higher for this species under such conditions 39 , 40 . Studies indicate that future climatic conditions in plateau are likely to trend toward a "wet-warm" climate with high precipitation and elevated temperatures 6 . In this context, the carbon sink capacity of Pinus densata remains robust. This indicates that this species will continue to play a significant role in carbon reduction and sequestration in the northwestern Yunnan in the future. The CSI of Pinus densata in Shangri-La has consistently remained above 10× 10 4 t C·hm⁻ 2 , except during the period from 2007 to 2012, indicating a stable and strong CSI over the years. This finding is consistent with result of Yin et al. 41 . However, during certain periods, such as 1992–1997 and 2007–2012, there was a slight decline in CSI. This decrease can be attributed to significant natural disasters during those times, including droughts, landslides, and earthquakes. These disasters led to vegetation damage and a reduction in vegetation cover 42 , consequently resulting in decreased carbon sequestration capacity. In addition to the influences of the natural factors, the variations in CSI of Pinus densata in the study area are also related to anthropogenic factors. To protect forest resources, a series of ecological construction projects have been implemented in Southwest China, including the "Grain for Green Program", "Natural Shelterbelt Program", and "Returning Grazing Land to Grassland" initiative 43 . As a result of these policies, the quality of forest and carbon density have improved, thereby ensuring the stability of forest carbon sink in Shangri-La 44 . With the development of the socio-economic, the scope and intensity of human activities have continually increased, leading to a declining trend in CSI of Pinus densata in certain areas, such as Jiantang, Hutiaoxia, and Xiaozhongdian. The rapid growth of the tourism industry has stimulated economic expansion, resulting in a pronounced population aggregation effect and a dramatic increase in urban and rural construction land, which has encroached upon substantial agricultural land 45 . This alteration in land use patterns has contributed to a decrease in carbon density of Pinus densata . Therefore, the bidirectional impact of anthropogenic factors on the carbon sink capacity of Pinus densata is an important area for future research. Conclusions In this article, we estimated the CSI of Pinus densata in Shangri-La from 1987 to 2017. We obtained ground data from the National Forest Inventory (NFI) to calculate the variation of AGCS of Pinus densata . We also extracted remote sensing factors from Landsat time series images to calculate their variation. Subsequently, we established dynamic models using RF and GA-RF based on these variations, selecting the optimal model for CSI estimation. We suggest that the GA significantly improves the estimation accuracy of the RF dynamic model, with the GA-RF model based on annual average changes achieving the best fitting, and making it suitable for CSI estimation. Meanwhile, the CSI of Pinus densata in Shangri-La has a CSI between 7.84 and 12.35×10 4 t C·hm − 2 from 1987 to 2017, exhibiting a fluctuating downward trend, yet it remains at a stable high level over the years. CSI is higher in northern and central Shangri-La. This reflects the fact that high latitude and altitude contribute to the growth of Pinus densata and increase its carbon sink capacity.. We applied correlation analysis methods to explore climate-driven mechanisms of CSI in Pinus densata forests. The results indicate that precipitation is the main factor contributing to CSI in Pinus densata forests. Temperature and surface solar radiation also had an effect on CSI. Combining the results of multiple analyses, we found that the effect of triple climate factors on CSI is much stronger. The weak drive of the triple factor is the main driver. This suggests that multifactorial synergy is the main driving mechanism. Thus, Quantitative analysis of the synergistic effects of multiple factors on carbon sink should be strengthened in future studies. Methods Study area Shangri-La is located in the northwestern Yunnan Province, at the southern edge of the Tibetan Plateau and the heart of the Hengduan Mountains, where Yunnan, Sichuan, and Tibet converge, covering a total area of 11,613 km 2 (Fig. 1 ). The climate in this region is influenced by the southwest Indian Ocean monsoon, with the rainy season from June to October and the dry season from November to the following May. Shangri-La's terrain is mainly highland and mountainous, with an average altitude of more than 3,000 m above sea level. There are a total of 43 tree species, including 10 coniferous species and 33 broadleaf species. Pinus densata is one of the dominant tree species in the area, accounting for 16.2% of the forested area 17 . The Pinus densata forest of Shangri-La exhibits strong representativeness and significance in vertical distribution. Therefore, this study contributes to understanding the CS potential of Pinus densata in northwestern Yunnan. Collection and processing of sample plots We employed the NFI data as the ground survey data, which is collected in 1987, 1992, 1997, 2002, 2007, 2012 and 2017. The number of sample plots for Pinus densata measured in each year is as follows: 19, 22, 23, 16, 16, 17, and 23, totaling 136 plots, which include some remeasured plots. Each plot measures 28.28 m × 28.28 m and is recorded using the Beijing 54 coordinate system. The data includes average diameter at breast height (DBH), average tree height (H), and the number of Pinus densata . Due to the absence of 7 consecutive repeated measurements in some fixed plots, we selected 22 fixed plots with 7 consecutive repetitions as baseline data 25 . Additionally, the distribution data of Pinus densata in Shangri-La was obtained from the China Forest Management Inventory. The distribution range of Pinus densata and the location of sample plots by year are shown in Fig. 11 . Due to the difficulty in collecting understory vegetation and soil data, sample plots data is limited to aboveground components. Therefore, this study primarily focuses on the aboveground carbon sink. AGB of the sample plots was calculated by the anisotropic growth equation developed from the project team 17 . First, we calculated the average AGB using the average tree height and average diameter at breast height (DBH) of the plots, and then computed the total AGB of the plots based on the average AGB and the number of Pinus densata . The AGB equation was as follows: AGB = 0.073 × DBH 1.739 × H 0.880 × P (1) Where, AGB stands for aboveground biomass (t), DBH stands for diameter at breast height (cm), H stands for tree height (m), and P is the number of Pinus densata . We screened the sample plots data through the calculation results, in which 5 plots with too small AGB (AGB < 1 t·hm − 2 ) were excluded, and then 6 plots with outliers were screened and excluded according to Pauta's criterion, in which a value is considered as an outlier if it exceeds three times the standard deviation of the mean value 25 . Finally, we can get 125 plots. The AGCS of the sample plots was calculated by multiplying the AGB by the carbon content rate. According to the Guidelines for Carbon Stock Measurement in Forest Ecosystems issued by the State Forestry and Grassland Administration 46 , the average carbon content of Pinus densata dry matter was 0.501. The calculation formula was as follows: C stock =AGB × C (2) where C stock is the AGCS (t) in Pinus densata forest and C is the carbon content rate. The change of AGCS from year m to year n of the same plot is the aboveground carbon sink value of the plot during this period 47 . The formula is as follows: C sink =C stock, n -C stock, m (3) where C sink is the aboveground carbon sink (t) in Pinus densata forest, and n and m denote the two different years, n > m . The aboveground carbon sink can be divided by the area to obtain the carbon sink intensity (CSI). A positive value indicates a carbon sink, while a negative value signifies a carbon source 48 . Considering the significant differences in administrative areas among the townships in Shangri-La and the varying distribution ranges of Pinus densata , we adopted CSI as the standard, so that the carbon sink capacity of Pinus densata in each township can be more objectively reflected. Collection and processing of remote sensing data We obtained Landsat 5 TM and Landsat 8 OLI time series images from the Geospatial Data Cloud website ( https://www.gscloud.cn ), covering a total of 7 time periods and 21 views (Table 4 ). The spatial resolution of the images is 30 m. For each year, we selected the three images with the lowest cloud coverage to ensure minimal cloudiness in the chosen images. To improve the image quality, we pre-processed all the images: firstly, the initial DN (digit number) values were converted to radiometric values using the Radiometric Correction Tool to remove the effects of the sensors 49 ; and atmospheric corrections were performed using the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) module 50 . Then, geometric correction was performed on each image with reference to the calibrated SPOT-5 image, the image coordinate system was corrected to the Beijing 1954 coordinate system to eliminate geometric errors, and the SPOT-5 image was re-sampled to a resolution of 30 m×30 m by bilinear interpolation to ensure that the error was less than 1 pixel; finally, terrain correction was performed using the slope matching model 51 . After terrain correction, the differences in radiance values of the images due to terrain relief are eliminated, and the images can better reflect spectral features 25 . Finally, the preprocessed images were stitched together by corresponding years. Table 4 Landsat time-series images Sensor type Year Data identification Stirp/Line Acquisition time Landsat5 TM 1987 LT51320401987364BKT00 132/40 30 December 1987 1987 LT51310411987357BJC01 131/41 23 December 1987 1987 LT51320411987364BKT00 132/41 30 December 1987 1992 LT51320401991311BKT00 132/40 16 November 1991 1992 LT51310411991320BKT00 131/41 7 November 1991 1992 LT51320411991311BKT00 132/41 16 November 1991 1997 LT51320401997279BKT00 132/40 6 October 1997 1997 LT51310411997320BKT01 131/41 16 November 1997 1997 LT51320411997311BKT00 132/41 7 November 1997 2002 LT51320402002005BJC00 132/40 5 January 2002 2002 LT51310412002302BJC00 131/41 29 October 2002 2002 LT51320412002005BJC00 132/41 5 January 2002 2007 LT51320402007003BJC01 132/40 3 January 2007 2007 LT51310412007060BJC00 131/41 1 March 2007 2007 LT51320412006288BJC00 132/41 15 October 2006 2012 LT51320402011014BKT00 132/40 14 January 2011 2012 LT51310412011007BKT00 131/41 7 January 2011 2012 LT51320412011286BKT00 132/41 13 October 2011 Landsat8 OLI 2017 LC08_L2SP_132040_20171216_20200902_02_T1 132/40 16 December 2017 2017 LC08_L2SP_131041_20171225_20200902_02_T1 131/41 25 December 2017 2017 LC08_L2SP_132041_20171216_20200902_02_T1 132/41 16 December 2017 Table 5 Remote sensing factors information. Where B1 is the blue band, B2 is the green band, B3 is the red band, B4 is the near-infrared band, B5 is the shortwave infrared-1 band, and B7 is the shortwave infrared-2 band. Feature types Factor types Remote sensing factors Texture features Gray-level co-occurrence Matrix Homogeneity (HO); Dissimilarity (DI); Mean (ME); Angular second moment (SM); Entropy (EN); Correlation (CC); Variance (VA); Contrast (CO) Filtering of probabilistic statistics Skewness (SK) Spectral features General vegetation index factors NDVI = (B4 − B3)/(B4 + B3); ND32 = (B3 − B2)/(B3 + B2); ND54 = (B5 − B4)/(B5 + B4); ND53 = (B5 − B3)/(B5 + B3); ND57 = (B5 − B7)/(B5 + B7); ND452 = (B4 + B5 − B2)/(B4 + B5 + B2); DVI = B4 − B3; RVI = B4/B3; RVI = B4/B3; ARVI = (B4 − (2B3 − B1))/(B4 + (2B3 − B1)) Information enhancement factors Principal component analysis (PCA1, PCA2, PCA3PCA4, PCA5, PCA7); VIS123 = B1 + B2 + B3; MID = B5 + B7; Albedo = B1 + B2 + B3 + B4 + B5 + B7; MID57 = B5 + B7 K-T Simple ratio vegetation indices B4/B2、B5/B3、B5/B4、B5/B7、B7/B3、B3/Albedo、B4×B3/B7 Original band factors B1、B2、B3、B4、B5、B7 To obtain the most relevant modelling factors for forest carbon sink, we extracted two types of remote sensing factors based on extensive literature review 25 , 52 : spectral feature factors and texture feature factors. In total, 35 spectral factors and 540 texture factors (various types of textures computed from all single bands from 1 to 19 odd windows of each image) were extracted (Table 5 ). Collection and processing of Meteorological data The meteorological data includes the 1 km resolution monthly average precipitation dataset for China (1901–2022), the 1 km resolution monthly average temperature dataset for China (1901–2022), and the regional high-resolution (10 km) surface solar radiation dataset for China (1983–2017). These data were obtained from the National Tibetan Plateau Data Center ( https://data.tpdc.ac.cn ), with a horizontal accuracy of 30 m and a vertical accuracy of 20 m. To standardize the coordinate system and resolution with other datasets, the meteorological data were projected to the Beijing 1954 coordinate system and then resampled to a resolution of 30 m 53 . Modeling process We employed three types of variation to establish the carbon sink model: 5-year interval variation, 10-year interval variation, and annual average variation. The calculation of the variation was based on two-year co-equal plots: we calculated the variation of the AGCS of the sample plots when the data were recorded in both calculation cycles, while the rest of the non-continuous samples we did not calculate 25 . In dynamic models, the variation in remote sensing factors was used as the independent variable, and the variation of the AGCS as the dependent variable. The formulas for calculating the variations are as follows: ∆V = V n - V m (4) $$\:\varDelta\:{V}_{a}=\frac{\varDelta\:V}{n-m}$$ 5 Where ∆V is the interval variation value, in which the variation is calculated, V n and V m are the data values of year n and year m , respectively, and ∆V a is the annual average change value. Using the above two equations to calculate the AGCS variation and the corresponding remote sensing factors variation of each sample plots. The variation of AGCS was used as the CS value. The shortest time interval for calculating change data is 5 years, while the longest is 30 years. However, there are few continuous sample plots with time intervals of 15 years or more, and the sample size is insufficient to support the experiment. We processed the 575 extracted remote sensing factors. Pearson correlation analyses were performed after calculating their 5-year interval, 10-year interval, and annual average variation. The effectiveness of the model during operation is influenced by the number of features. An excessive number of features can slow down model fitting, increase computational workload, and ultimately affect model stability 10 . The original features contain some redundant information, which can decrease model accuracy and lead to negative effects such as overfitting 54 . Conversely, too few variables can hinder the correct construction of the model. To avoid the risks of overfitting and covariance, we employed a stepwise regression method 10 . Following the methods of researchers 25 , 52 , we ultimately selected 10 strongly correlated remote sensing factors, all of which exhibited highly significant correlations ( P < 0.01) for modeling. RF is one of the most effective non-parametric regression models 55 . Compared to parametric regression methods, this method does not require testing assumptions such as the normality and independence of variables 54 . It can avoid overfitting, perform better with outliers, and handle high-dimensional data 56 . Additionally, it can assess the importance of each feature in the model. First, we need to adjust the model parameters, which include four key parameters: the maximum number of iterations (n_estimators), the maximum depth of the decision trees (max_depth), the minimum number of samples at leaf nodes (min_samples_leaf), and the minimum number of samples required to split an internal node (min_samples_split). GA is an adaptive heuristic search algorithm that is part of evolutionary algorithms, based on the principles of natural selection and genetics 16 . It applies with historical data provided by random searches to guide the search toward regions of the solution space that perform better. Typically, GA is used to generate high-quality solutions for optimization and search problems 55 , thereby avoiding the subjective tuning of hyperparameters associated with regular RF models. The main operation process is divided into four steps: iterations, mutation, crossover and selection. Compared with regular RF models, GA assigns a weight vector to all variables in the feature space to evaluate their importance metrics 15 . By optimizing the parameter selection of RF using GA, we established the GA-RF models (Fig. 12 ). The main parameters of the GA-RF models involve four key components: population, iterations, mutation, and crossover. The evaluation of model accuracy is divided into two parts: model fitting and model estimation effect test. 80% of the data are randomly selected for model fitting and 20% for model estimation effect test in each modelling. The adopted model accuracy evaluation indexes include the coefficient of determination ( R 2 ), root mean square error (RMSE), relative root mean square error (rRMSE), and prediction accuracy ( P ). The calculation formulas are as follows 19 : $$\:{R}^{2}=\frac{\sum\:_{i=1}^{n}{(\widehat{{y}_{i}}-\stackrel{-}{y})}^{2}}{\sum\:_{i=1}^{n}{({y}_{i}-\stackrel{-}{y})}^{2}}$$ 6 $$\:\text{R}\text{M}\text{S}\text{E}=\sqrt{\frac{\sum\:_{i=1}^{n}{({y}_{i}-\widehat{{y}_{i}})}^{2}}{n}}$$ 7 $$\:\text{r}\text{R}\text{M}\text{S}\text{E}=\frac{\text{R}\text{M}\text{S}\text{E}}{\stackrel{-}{y}}\times\:100\%$$ 8 $$\:P=\frac{1}{n}\sum\:_{i=1}^{n}\left(1-\left|\frac{{y}_{i}-\widehat{{y}_{i}}}{\widehat{{y}_{i}}}\right|\right)\times\:100\%$$ 9 Where, \(\:{y}_{i}\) represents the true value, \(\:\widehat{{y}_{i}}\) represents the model regression value, \(\:\stackrel{-}{\:y}\) is the true value mean, and n is the number of samples. Analysis of driving forces We used a correlation analysis method to calculate the degree of correlation between MAP, MAT and SSR and forest CSI based at the pixel scale with the following Eq. 5 7 : $$\:{R}_{xy}=\frac{\sum\:_{i=1}^{n}\left[\left({x}_{i}-\stackrel{-}{x}\right)\left({y}_{i}-\stackrel{-}{y}\right)\right]}{\sqrt{\sum\:_{i=1}^{n}{({x}_{i}-\stackrel{-}{x})}^{2}}\times\:\sqrt{\sum\:_{i=1}^{n}{({y}_{i}-\stackrel{-}{y})}^{2}}}$$ 10 Where, R xy represents the correlation coefficient of variables x and y, and its value range is [-1,1]. When the correlation coefficient is less than 0, it is called negative correlation; when it is greater than 0, it is called positive correlation. When it is equal to 0, it is irrelevant. The closer the absolute value is to 1, the stronger the correlation. x i is the CSI in the year i (t C·hm − 2 ), and y i is the climatic factor for year i. In order to determine the effect of a single factor on CSI without interference from other factors, we used partial correlation analysis for further study 58 . The formula is as follows: $$\:{r}_{x{y}_{1},{y}_{2}}=\frac{{R}_{x{y}_{1}}-{R}_{xy2}\times\:{R}_{{y}_{1}{y}_{2}}}{\sqrt{(1-{R}_{x{y}_{2}}^{2})(1-{R}_{{y}_{1}{y}_{2}}^{2})}}$$ 11 Where, \(\:{r}_{x{y}_{1},{y}_{2}}\) represents the first-order partial correlation coefficient between the CSI and the climatic factor y 1 excluding the effect of climatic factor y 2 . To discuss the partial correlation coefficients between CSI and the three variables, the second-order partial correlation coefficient should be considered with the following formula 5 : $$\:{r}_{x{y}_{1},{y}_{2}{y}_{3}}=\frac{{r}_{x{y}_{1},{y}_{2}}-{r}_{x{y}_{3},{y}_{2}}\times\:{r}_{{{y}_{1}y}_{3,}{y}_{2}}}{\sqrt{(1-{r}_{x{y}_{3},{y}_{2}}^{2})(1-{r}_{{y}_{1}{y}_{3}{,y}_{2}}^{2})}}$$ 12 Where, \(\:{r}_{x{y}_{1},{y}_{2}{y}_{3}}\) represents the second-level partial correlation coefficient between CSI and climatic factor y 1 excluding the effect of climate factors y 2 and y 3 . For example, the correlation between CSI and MAP after excluding the double effects of MAT and SSR. The range of partial correlation coefficient is also [-1,1], less than 0 is negative correlation, more than 0 is positive correlation, and the closer the absolute value is to 1, the closer the degree of partial correlation is. The significance test was performed by T-test with the following formula 59 : $$\:t=\frac{r\times\:\sqrt{n-m-1}}{\sqrt{1-{r}^{2}}}$$ 13 Where, r is the partial correlation coefficient, n is the number of samples, 6(six periods), and m is the number of independent variables, 1. In this paper, the level of significance of the t-test results was set at α = 0.05 60 . The correlation coefficients between CSI and climate factors were classified as highly significant ( P < 0.01), significant (0.01 ≤ P < 0.05) and non-significant ( P ≥ 0.05) according to the significance level. Multi-correlation coefficient is an indicator that reflects the degree of correlation between a dependent variable and a set of independent variables (two or more). Moreover, it is a measure of the degree of multi-correlation 60 . The formula was calculated as follows: $$\:MR=\sqrt{1-（1-{R}_{x{y}_{1}}^{2}）(1-{r}_{x{y}_{2},{y}_{1}}^{2})(1-{r}_{x{y}_{3},{y}_{1}{y}_{2}}^{2})}$$ 14 Where, MR is the multi-correlation coefficient between the three climate factors and CSI. Its value range is [0,1], the larger the multi-correlation coefficient, the closer the linear correlation between the variables. The significance test of the multi-correlation coefficient was performed by F-test method with the following formula 59 : $$\:F=\frac{MR}{1-MR}\times\:\frac{n-m-1}{m}$$ 15 To accurately investigate the driving mechanisms of climate change on the CSI of Pinus densata in Shangri-La, we referenced the classification methods used by other researches in Southwest China 59 , 60 . Subsequently, based on the significance analysis results of partial correlation and multi-correlation, we established the classification criteria for nine climate driving factors (Table 6 ). Table 6 Rules of climactic driving factors for CSI of Pinus desata Climate driving type Criteria for classification Partial correlation coefficient Multi-correlation coefficient PR CSI−Pre PR CSI−Tem PR CSI−SSR MR CSI−Pre+Tem+SSR Single-factor driver MAP only t < 0.05 t ≥ 0.05 t ≥ 0.05 F < 0.05 MAT only t ≥ 0.05 t < 0.05 t ≥ 0.05 F < 0.05 SSR only t ≥ 0.05 t ≥ 0.05 t < 0.05 F < 0.05 Double-factor driver Double: MAP, MAT t < 0.05 t < 0.05 t ≥ 0.05 F < 0.05 Double: MAP, SSR t < 0.05 t ≥ 0.05 t < 0.05 F < 0.05 Double: MAT, SSR t ≥ 0.05 t < 0.05 t < 0.05 F < 0.05 Triple-factor driver Triple Strong Drive t < 0.05 t < 0.05 t < 0.05 F < 0.05 Triple Weak Drive t ≥ 0.05 t ≥ 0.05 t ≥ 0.05 F < 0.05 Non-climatic driver t ≥ 0.05 t ≥ 0.05 t ≥ 0.05 F ≥ 0.05 Declarations Data Availability The Landsat TM and Landsat OLI data are available through https://www.gscloud.cn/ (accessed on 23 October 2024) and The meteorological data are available through https://data.tpdc.ac.cn/home (accessed on 23 October 2024). NFI data presented in this study are available on request from the corresponding author; the data are not publicly available due to the confidentiality of the dataset. Acknowledgements This work was funded by the National Natural Science Foundation of China (No. 32260390); “Young Top Talents” special project of the high-level talent training support program of Yunnan province, China, in 2020 (No. YNWR-QNBJ-2020-164); Innovation Programs of Southwest Forestry University (Grant No:LXXK-2023Z06). Authorship contribution statement K. Y. conceptualized the study, processed the data, designed the experiment and wrote the original draft. K.L. processed the data and analyzed results. J.Z. collected data, provided resources and edited the manuscript. B.Q. analyzed results. F.W. , Q.X. , J.C., Y.H. and J.Y. edited the manuscript. All authors reviewed the manuscript. Competing interests The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5315691","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":381032237,"identity":"ddbdd64c-ddec-479e-8341-9b5e6b905055","order_by":0,"name":"Kun Yang","email":"","orcid":"","institution":"China、Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Yang","suffix":""},{"id":381032238,"identity":"02886b17-f775-4bed-b307-543489b1fd2c","order_by":1,"name":"Kai Luo","email":"","orcid":"","institution":"China、Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Luo","suffix":""},{"id":381032239,"identity":"dc28c4f9-949e-48a9-8e33-96271c4411d7","order_by":2,"name":"Jialong Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYBACxmYGxgMghgGI+MDAwA+iJQhoYYBrYZzBwCDZQEgLCMC1MPMQo4W5nfnBgY87au3N2c8efm3z57CEwQHmg7d5GOzycDuMzeDgzDPHmS178tKsc9tAWtiSrXkYkovx+MXgMG/bMTaDAzlmxrkNt+sMDvCYSfMwHEhswKmF/QNIC4/B+TdmxhZ/bgNt4f9GQAsPyJYaCYMbOcaPGdhAWnjYCGkpODiz7YCBwY03Zoy9bf8lJA+zGVvOMUjGqcWw//jGBx/b6uwNzucYf/jxJ02C73jzwxtvKuxwa4FIHAYRbJDoYAYRBjjUA4E8hKoDq/2AW90oGAWjYBSMZAAASK5ax+VowHMAAAAASUVORK5CYII=","orcid":"","institution":"China、Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Jialong","middleName":"","lastName":"Zhang","suffix":""},{"id":381032240,"identity":"35ba286f-8624-45ef-8c3d-bdca17fc6803","order_by":3,"name":"Bo Qiu","email":"","orcid":"","institution":"China、Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Qiu","suffix":""},{"id":381032241,"identity":"d86634c4-f63f-422f-9200-e70bc3b32bef","order_by":4,"name":"Feiping Wang","email":"","orcid":"","institution":"Guangxi State-owned Gaofeng Forest Farm","correspondingAuthor":false,"prefix":"","firstName":"Feiping","middleName":"","lastName":"Wang","suffix":""},{"id":381032242,"identity":"41bee9d7-1b78-4059-9658-e0c1d89cac1d","order_by":5,"name":"Qinglin Xiao","email":"","orcid":"","institution":"China、Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Qinglin","middleName":"","lastName":"Xiao","suffix":""},{"id":381032243,"identity":"8993a1e4-9fcd-4496-9c8d-56cc3acfa068","order_by":6,"name":"Jun Cao","email":"","orcid":"","institution":"China、Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Cao","suffix":""},{"id":381032244,"identity":"0cf7147a-ae36-4b49-b978-c8be824815ac","order_by":7,"name":"Yunrun He","email":"","orcid":"","institution":"China、Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Yunrun","middleName":"","lastName":"He","suffix":""},{"id":381032245,"identity":"49a99522-9735-4912-b73a-369b691924eb","order_by":8,"name":"Jian Yang","email":"","orcid":"","institution":"State-owned Jiaozuo Forest Farm","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-10-23 05:08:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5315691/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5315691/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-84258-7","type":"published","date":"2025-01-02T15:57:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69648916,"identity":"c19358c0-4f72-48a7-8519-ceff40f759cd","added_by":"auto","created_at":"2024-11-22 15:38:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4530266,"visible":true,"origin":"","legend":"\u003cp\u003eLocation and climate information map of the study area. (\u003cstrong\u003ea\u003c/strong\u003e) overview of Shangri-La's administrative divisions; (\u003cstrong\u003eb\u003c/strong\u003e) Shangri-La location overview map in Yunnan province, China; (\u003cstrong\u003ec\u003c/strong\u003e),\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003ed\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eand (\u003cstrong\u003ee\u003c/strong\u003e) map of total meteorological averages value from 1987-2017: mean annual precipitation (MAP), mean annual temperature (MAT), and (\u003cstrong\u003ee\u003c/strong\u003e) mean annual surface solar radiation (SSR).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5315691/v1/9ec0abb17ac2e54863f0c762.png"},{"id":69648915,"identity":"a7b35ff6-9d2f-48f2-a391-f74c50930a3e","added_by":"auto","created_at":"2024-11-22 15:38:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1556966,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of the study. (DW: Dongwang; GZ: Geza; HTX: Hutiaoxia; JT: Jiantang; LJ: Luoji; NX: Nixi; SB: Sanba; XZD: Xiaozhongdian; SJ: Shangjiang; JJ: Jinjiang; WJ: Wujing. The same as below.)\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5315691/v1/5d3c1f1fc9bc63bd5ec63765.png"},{"id":69648921,"identity":"369a651b-e392-410a-ac62-953ca5bbe939","added_by":"auto","created_at":"2024-11-22 15:38:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":11630172,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation and contribution degree of each model's modeling factors. (\u003cstrong\u003ea\u003c/strong\u003e) and (\u003cstrong\u003eb\u003c/strong\u003e), top 10 factors in the 5-year variation; (\u003cstrong\u003ec\u003c/strong\u003e) and (\u003cstrong\u003ed\u003c/strong\u003e), top 10 factors in the 10-year variation; (\u003cstrong\u003ee\u003c/strong\u003e) and (\u003cstrong\u003ef\u003c/strong\u003e), top 10 factors in the Annual average variation. (\u003cstrong\u003ea\u003c/strong\u003e), (\u003cstrong\u003ec\u003c/strong\u003e) and (\u003cstrong\u003ee\u003c/strong\u003e), the results of Pearson's correlation analysis. (\u003cstrong\u003eb\u003c/strong\u003e), (\u003cstrong\u003ed\u003c/strong\u003e) and (\u003cstrong\u003ef\u003c/strong\u003e), the contributions of modelling factors.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5315691/v1/f6945857bec44108b41d46d5.png"},{"id":69648923,"identity":"a4687e2c-5f12-48c0-9531-133b9e4050eb","added_by":"auto","created_at":"2024-11-22 15:38:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":12660088,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of dynamic modelling results.\u003c/p\u003e","description":"","filename":"FIgure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5315691/v1/dea0298bb1262df6218e77fb.png"},{"id":69648924,"identity":"3c0d158f-e9fb-4164-b467-945bad5ce512","added_by":"auto","created_at":"2024-11-22 15:38:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3655541,"visible":true,"origin":"","legend":"\u003cp\u003eShangri-La's CSI by periods (Period 1 is 1987-1992, Period 2 is 1992-1997, Period 3 is 1997-2002, Period 4 is 2002-2007, Period 5 is 2007-2012 and Period 6 is 2012-2017).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5315691/v1/b3293844ac0723b5b7dfbbd3.png"},{"id":69648922,"identity":"8ffb6f4b-9ba3-4b81-9126-99ec50b3001b","added_by":"auto","created_at":"2024-11-22 15:38:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4405703,"visible":true,"origin":"","legend":"\u003cp\u003eCSI in Shangri-La townships in all time periods, 1987-2017. (\u003cstrong\u003ea\u003c/strong\u003e) changes in CSI by township between 1987-2017; (\u003cstrong\u003eb\u003c/strong\u003e) percentage of CSI by township in each period. OT: Other townships. Here OT refers to the sum of the CSI of the Sanjiang, Jinjiang and Wujing\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5315691/v1/8534e150b87674c96ed53ee6.png"},{"id":69649284,"identity":"618ea261-c414-41d5-a1c3-d2ec5472e1df","added_by":"auto","created_at":"2024-11-22 15:46:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2379941,"visible":true,"origin":"","legend":"\u003cp\u003ePartial correlation between CSI and climatic factors. (\u003cstrong\u003ea\u003c/strong\u003e) CSI and MAP; (\u003cstrong\u003eb\u003c/strong\u003e) CSI and MAT; (\u003cstrong\u003ec\u003c/strong\u003e) CSI and SSR.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5315691/v1/6ab54c06b83142afbfded654.png"},{"id":69648917,"identity":"912299bb-ec86-4567-8874-bad5c11394ec","added_by":"auto","created_at":"2024-11-22 15:38:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2408785,"visible":true,"origin":"","legend":"\u003cp\u003eSignificance of partial correlation between CSI and climatic factors. (\u003cstrong\u003ea\u003c/strong\u003e) CSI and MAP; (\u003cstrong\u003eb\u003c/strong\u003e) CSI and MAT; (\u003cstrong\u003ec\u003c/strong\u003e) CSI and SSR.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5315691/v1/59b8e192f9f0deda2fdb3670.png"},{"id":69648925,"identity":"4fd9efd7-9a0f-4739-bf8a-779437ef1774","added_by":"auto","created_at":"2024-11-22 15:38:53","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4108842,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-correlation between CSI and climate factors and climate-driven mechanisms of \u003cem\u003ePinus desata\u003c/em\u003ein Shangri-La. (\u003cstrong\u003ea)\u003c/strong\u003e Multi-correlation between CSI and climatic factors; (\u003cstrong\u003eb)\u003c/strong\u003eclimate-driven types of zoning in CSI and their area share.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-5315691/v1/f3d49e5012343a0af7d2ad44.png"},{"id":69648919,"identity":"0b211024-0932-44bf-9611-22ab92c75735","added_by":"auto","created_at":"2024-11-22 15:38:52","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":548043,"visible":true,"origin":"","legend":"\u003cp\u003eArea share of climate-driven mechanism types for CSI change in \u003cem\u003ePinus densata\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-5315691/v1/f677fecffabe8f781abb20fc.png"},{"id":69649287,"identity":"56199fca-c4ae-4a88-bc8f-9da1e4275540","added_by":"auto","created_at":"2024-11-22 15:46:53","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":3714674,"visible":true,"origin":"","legend":"\u003cp\u003eSample plots distribution map.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-5315691/v1/340b1b1081276991194b2f6e.png"},{"id":69648918,"identity":"bc17fbbf-1bf1-4612-9596-1c7eb2f4a3c6","added_by":"auto","created_at":"2024-11-22 15:38:51","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":330832,"visible":true,"origin":"","legend":"\u003cp\u003eModelling process of the GA-RF\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-5315691/v1/67d74744376caf6d8cd34250.png"},{"id":73093311,"identity":"fb87a598-596f-4ded-b553-fa26fecb28a0","added_by":"auto","created_at":"2025-01-06 16:13:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":53100582,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5315691/v1/8c158730-32ef-4b97-9e65-cd93bb2c40f4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimating forest aboveground carbon sink based on Landsat time-series and its response to climate change","fulltext":[{"header":"Introduction","content":"\u003cp\u003eForest, as a major component of terrestrial ecosystem, possess significant carbon sink potential\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Forest carbon sink refers to the process by which green plants absorb CO\u003csub\u003e2\u003c/sub\u003e from the atmosphere through photosynthesis and sequester it within the forest, playing a crucial role in global climate change mitigation\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Forest carbon sink accounts for 76\u0026ndash;98% of terrestrial carbon sink (CS), holding an important position in the global carbon sink landscape\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In recent years, forest carbon sink have garnered widespread attention due to their substantial economic and ecological value, becoming a vital strategy for addressing climate change and achieving carbon neutrality\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Effective monitoring and assessment of forest carbon sink capacity are essential for maintaining the carbon balance of terrestrial ecosystems, achieving efficient management of forest resources and mitigating climate warming\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe estimation of forest carbon sink can be achieved through quantitative analysis of variations in forest carbon stock over specific periods\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Remote sensing-based estimation is one of the best methods for large-scale and long-term studies\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Since the launch of the first Landsat satellite in 1972, its long lifespan and good data continuity have made it an ideal choice for time series research\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Machine learning algorithms are important tools for estimating CS based on remote sensing images, widely used in assessing forest resources such as aboveground biomass (AGB) and carbon stock\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Models established by Random Forest (RF) method demonstrate higher stability and accuracy\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, effectively avoiding the underestimation of CS seen in some ecosystem process models\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Parameter adjustment is a common strategy for improving machine learning-based models\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Previous studies have shown that optimizing parameter selection by genetic algorithms (GA) is also an effective method to enhance model accuracy\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, not every algorithm can be adapted to the model. A comparative analysis is needed to determine whether the optimization of the algorithm meets the experimental requirements.\u003c/p\u003e \u003cp\u003eDynamic models are increasingly gaining attention in the field of forest remote sensing monitoring due to their strong data correlation over long time series\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Researches indicated that dynamic models perform better in estimating AGB\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Meanwhile, it allows for evaluation of time-dependent phenomena and period needed for recovery\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. While steady-state models remain the foundation for forest resource estimation, dynamic models provide researchers with new perspectives. Currently, studies applying dynamic models to forest carbon sink estimation are relatively rare, making this attempt highly valuable. Given that forest carbon sink is dynamic variable, we established a dynamic model based on this theory to accurately analyze the long-term changes in forest carbon sink.\u003c/p\u003e \u003cp\u003eGlobal warming has become an undeniable fact, and with the intensification of climate change, the capacity of terrestrial CS will be significantly affected\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. China's terrestrial CS is particularly sensitive to climate change, forest carbon sink is even more significantly influenced by climate change\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Long-term and severe climate changes may lead to a weakening of forest carbon sink. Lack of precipitation and prolonged droughts can reduce the carbon absorption capacity of large forests such as the African rainforest and the Amazon rainforest\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, with some regions potentially shifting to being carbon sources\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Quantifying the impact of climate factors on forest carbon sink is crucial for ecological protection and sustainable development. However, accurately quantifying the spatial and temporal effects of climate factors on forest carbon sink remains a major challenge in current research.\u003c/p\u003e \u003cp\u003eShangri-La is a typical ecological carbon sink in China's Yunnan Province, and its geomorphologic and climatic characteristics are highly representative of southwestern China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cem\u003ePinus densata\u003c/em\u003e is one of the dominant tree species in Shangri-La. In addition to Yunnan, \u003cem\u003ePinus densata\u003c/em\u003e is also widely distributed in Sichuan and Tibet, and plays an important role in the carbon cycle in southwest China. Shangri-la is located in the southwest of the Qinghai-Tibet Plateau, with complex terrain, changeable climate and fragile ecological environment, so it is difficult to accurately estimate forest carbon sink intensity (CSI). Besides, the climate driving mechanism of carbon sink of \u003cem\u003ePinus densata\u003c/em\u003e is still unclear. Therefore, in this study, remote sensing-based dynamic models were used to estimate the carbon sink capacity of \u003cem\u003ePinus densata\u003c/em\u003e in Shangri-La, and correlation analysis methods were used to explore the climate driving mechanism (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The main objectives are the following: (1) establishing carbon sink dynamic models based on three different types of variation; (2) analyzing the effect of GA on RF dynamic models; (3) estimating the carbon sink of \u003cem\u003ePinus densata\u003c/em\u003e in Shangri-La and analyze its temporal and spatial changes; (4) exploring the climate driving mechanism.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of modelling\u003c/h2\u003e \u003cp\u003eFor each of the three types of variation, we selected 10 of the strongest correlated remote sensing factors, all of which exhibited highly significant correlations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) suitable for modeling (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The optimal modelling factors with strong and highly significant correlations among the three types of variation were all texture feature factors, among which SK and SM appeared most frequently.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eScreening factor for each type of variation. Where the naming form of the texture factor is \u003cb\u003eRXBYFF\u003c/b\u003e, where \u003cb\u003eR\u003c/b\u003e is the texture window, \u003cb\u003eX\u003c/b\u003e is the size of the window, and the value of \u003cb\u003eX\u003c/b\u003e is an odd number within 1\u0026thinsp;~\u0026thinsp;19 (including 1 and 19); \u003cb\u003eBY\u003c/b\u003e is a certain single band of the image, and the value of Y is 1\u0026thinsp;~\u0026thinsp;5 and 7; \u003cb\u003eFF\u003c/b\u003e is the abbreviation of texture factor. For example, R9B4SK is the SK of the fourth band under the 9\u0026times;9 window.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariation type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRemote sensing factors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-year variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR9B4SK、R7B4SK、R9B4SM、\u003c/p\u003e \u003cp\u003eR3B3SK、R3B2SK、R17B1SM、\u003c/p\u003e \u003cp\u003eR3B4SK、R19B1SM、R15B1SM、R7B4CC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10-year variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR11B1SM、R7B1SM、R9B1SM、\u003c/p\u003e \u003cp\u003eR15B7SM、R17B7SM、R17B1SM、\u003c/p\u003e \u003cp\u003eR19B7SM、R19B1SM、R15B1SM、R7B2CC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual average variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR9B4SK、R7B4SK、R9B4SM、\u003c/p\u003e \u003cp\u003eR3B3SK、R3B2SK、R17B1SM、\u003c/p\u003e \u003cp\u003eR3B4SK、R19B1SM、R15B1SM、R13B1SM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe optimal parameters of each model of the RF established by the three variations respectively are shown in the Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The optimal parameters of the GA-RF models are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. GA-RF models were optimized to spend exponentially more time on training than RF models. In addition, all the six dynamic models ranked the input factors through their importance and output the modelling contribution values of each factor (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOptimum parameters of RF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariation type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en_estimators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emax_depth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emin_samples_leaf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emin_samples_split\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-year variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10-year variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual average variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOptimum parameters of GA-RF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariation type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en_estimators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emax_depth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emin_samples_leaf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emin_samples_split\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-year variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10-year variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual average variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe accuracy of the six dynamic models is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Among the dynamic models with each type of variation, the \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eP\u003c/em\u003e of the GA-RF model are greater than that of the RF model, and the RMSE is smaller than that of the RF. Combining the evaluation metrics, it can be found that the GA-RF fit based on the annual average variation is the best, which has improved the \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, lowered the RMSE, and improved the \u003cem\u003eP\u003c/em\u003e compared with the optimal conventional RF model. This shows that the accuracy of the RF-based dynamic model is improved after GA optimization.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, rRMSE and \u003cem\u003eP\u003c/em\u003e of GA-RF model based on annual mean change is better than that of other types of GA-RF model. Due to the different time scales, the RMSE should be standardized to the same scale when making comparisons \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. After multiplying the RMSE of the 5-year variation by 2 and the annual average variation by 10, we found that GA-RF based on the annual average change possesses the smallest RMSE. Thus the GA-RF model accuracy of the annual average variation is better than that of the models with the 5-year and 10-year variation, which is the optimal dynamic model.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEstimation of CSI\u003c/h3\u003e\n\u003cp\u003eFrom 1987 to 2017, we divided the period into six intervals of five years each. According to the model evaluation results, the GA-RF model based on annual average variation was used to estimate CSI. By integrating the data on \u003cem\u003ePinus densata\u003c/em\u003e distribution, we derived the spatial distribution map of CSI of \u003cem\u003ePinus densata\u003c/em\u003e in Shangri-La (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The CSIs for periods 1\u0026ndash;6, respectively, were 10.53\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, 12.35\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, 10.17\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, 11.75\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e. 7.84\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and 10.2\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom 1987 to 2017, CSI of \u003cem\u003ePinus densata\u003c/em\u003e in Shangri-La fluctuated between 7.84 and 12.35\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, with an overall average CSI of 10.48\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e. There was a general declining trend, decreasing from 10.53\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e in Period 1 to 10.2\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;a\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in Period 6. The period with the strongest carbon sink capacity was Period 2, with an CSI of 12.35\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, while the weakest period was Period 5, with an CSI of 7.84\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e. However, compared to the end of the last century, the overall CSI of \u003cem\u003ePinus densata\u003c/em\u003e in Shangri-La has tended to stabilize, showing little variation.\u003c/p\u003e \u003cp\u003eSignificant differences in CSI of \u003cem\u003ePinus densata\u003c/em\u003e are observed across various townships in Shangri-La, reflecting different carbon sink capacities (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Geza has the highest CSI, ranging from 1.78\u0026ndash;3.36 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm⁻\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Jiantang follows, with a CSI of 1.69\u0026ndash;2.54 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm⁻\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Both townships have average CSI greater than 2\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, classifying them as high CSI areas. In contrast, the average CSI of \u003cem\u003ePinus densata\u003c/em\u003e in the townships of Dongwang, Luoji, and Nixi fall between 1\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and 2\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, classifying them as moderate CSI areas. The remaining townships have CSI below 1\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and do not reach the average CSI across various periods, classifying them as low CSI areas.\u003c/p\u003e \u003cp\u003eThe CSI of \u003cem\u003ePinus densata\u003c/em\u003e in each township fluctuated during 1987\u0026ndash;2017 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Geza and Jiantang show significant variations in CSI but maintain high CSI over the years. Conversely, although Hutiaoxia, Xiaozhongdian, and Jinjiang had weaker CSI, they exhibited little variation during the study period, resulting in stable carbon sink capacities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eAnalysis of climate driving force\u003c/h3\u003e\n\u003cp\u003ePixel level analysis of the correlation of CSI of \u003cem\u003ePinus densata\u003c/em\u003e with MAP, MAT and SSR temporal changes from 1987 to 2017 based on the partial correlation coefficient and T-test (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The mean value of the partial correlation coefficient between MAP and CSI was 0.018. The area with positive correlation accounted for 51.41% of the total area. The positively correlated areas accounted for 35.65% of the area and were distributed throughout Shangri-La, mainly in the central region. The negatively correlated areas accounted for 48.59% of the total area, and the significantly negatively correlated areas accounted for 32.84% of the total area, which were distributed in the southern part of the study area with low CSI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe mean value of the partial correlation coefficient between MAT and CSI was \u0026minus;\u0026thinsp;0.015, with 48.24% of the area positively correlated, and 32.62% of the area significantly positively correlated. The proportion of negatively correlated areas was 51.76%, and the proportion of significantly negatively correlated areas was 36.31%, and both positively and negatively correlated areas were distributed throughout the whole territory, without being concentrated in a particular region.\u003c/p\u003e \u003cp\u003eThe mean value of the partial correlation coefficient between SSR and CSI was \u0026minus;\u0026thinsp;0.013. The positive correlation areas accounted for 45.09% of the total area, of which the significant positive correlation areas accounted for 24.77%, mainly in the central part of the study area. The area of negatively correlated areas accounted for 54.91%, of which 31.75% were significantly negatively correlated, mostly in the northern part of the study area.\u003c/p\u003e \u003cp\u003eThe mean multi-correlation coefficient between CSI and climate factors in Shangri-La is 0.65 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), showing that the comprehensive impact of climate change significantly promotes the growth of CSI of \u003cem\u003ePinus densata\u003c/em\u003e in this area. There are no notable spatial differences in the overall correlation between CSI and climatic factors. Regions with highly significant positive correlations account for 35.36% of the total area of the study region and are widely distributed throughout the area (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Shangri-La, 96.42% of the variation in CSI of \u003cem\u003ePinus densata\u003c/em\u003e is driven by climatic factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Among these, the areas jointly influenced by MAP, MAT and SSR account for the largest proportion, reaching 26.91%, and are widely distributed throughout the study area. In the single-factor driver, the proportion of areas driven by MAP only is 21%, significantly higher than those influenced by MAT and SSR. Specifically, areas driven by MAP only are primarily located in the northern and northeastern Shangri-La; areas driven by MAT only are mainly concentrated in the central; while areas influenced by SSR only are distributed across the central and southern Shangri-La. The areas influenced by other types of climatic drivers on CSI account for less than 10% and are dispersed within the study area.\u003c/p\u003e \u003cp\u003eIn summary, it is evident that among all driving factors, whether acting independently or in conjunction with others, MAP has the most significant impact on CSI. This indicates that precipitation is the primary driving factor affecting the CSI of \u003cem\u003ePinus densata\u003c/em\u003e. Furthermore, the area influenced by multiple factors exceeds that influenced by single factors, suggesting that climate factors primarily exert their effects on the CSI of \u003cem\u003ePinus densata\u003c/em\u003e through synergistic interactions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccurately estimating forest carbon sink has long been a contentious challenge in current research. Studies conducted in the same region often yield differing estimates of carbon sink capacity\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Due to the unique temporal and spatial characteristics of plateau and variations in estimation methods, there is considerable uncertainty in the accurate estimation of forest carbon sink in Shangri-La\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The complex topography of the plateau and various influencing factors on vegetation growth, including the location of regenerated forests, differences in forest age, and the rate of forest renewal, contribute to this uncertainty\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. These factors lead to significant discrepancies in estimates. Furthermore, long-term soil variability in the Tibetan Plateau can affect the growth of scattered conifers, resulting in quantifiable uncertainties regarding initial soil conditions, which may further impact carbon sink estimations\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Therefore, the processing of sample plots is a very important step in the modeling\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. We eliminated sample plots with too large or too small AGBs at the beginning to ensure that there were no young forests and no sample plots with too high densities. Subsequently, the Pauta criterion was used to remove anomalous data and reduce uncertainty.\u003c/p\u003e \u003cp\u003eCurrently, machine learning models have become important vehicles for remote sensing estimation methods. However, using machine learning models for estimation also has its limitations, such as parameter tuning, high data requirements, simulation accuracy, and model stability\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Additionally, the selection of model parameters and the timing of data collection are critical factors. While machine learning models can achieve good accuracy after parameter tuning, this state is not entirely stable and often requires multiple iterations of parameter adjustments\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Furthermore, varying data sources and data collected at different times can lead to significantly different results from the same model\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This raises higher demands for satellite data resolution, the sources of plot data, and the accuracy of the data. We chose three types of time interval variables to compare the effects of long, medium, and short time intervals on model accuracy. The results demonstrate that shorter time intervals minimize uncertainty in time series studies.\u003c/p\u003e \u003cp\u003eDifferences in estimation methods can lead to even greater uncertainties. Various estimation methods are influenced by factors such as the number and distribution of parameters, observation stations or plots, and scale conversion. These issues are particularly pronounced in plateau, resulting in significant disparities in CS estimates, which can exceed a factor of ten\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. For instance, Wang et al. estimated China's terrestrial CS using atmospheric inversion methods, yielding a result of 1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 Pg C\u0026middot;a⁻\u0026sup1;, which accounts for approximately 60% of the global CS during the same period\u0026mdash;substantially higher than estimates from other researchers at that time\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In contrast, Martin et al. employed the eddy covariance method, integrating machine learning techniques with carbon flux data obtained from the Global Flux Observatory Network (FLUXNET), to estimate the global Net Ecosystem Production (NEP) at 23 Pg C\u0026middot;a⁻\u0026sup1;, approximately eight times the global terrestrial CS\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In this study, the accuracy of the classic RF model is greatly improved by introducing GA for hyperparameter optimization. A number of evaluation indexes showed that the accuracy of the final model met the estimation requirements. The rationality of the model was further ensured based on the selection of the type of variation.\u003c/p\u003e \u003cp\u003eMoreover, models are unable to account for the relatively frequent natural disturbances in forests and recent reconstructions, and the datasets may not capture recovery areas following crop rotation or small-scale logging\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, reducing estimation uncertainty and exploring new methods with higher accuracy will continue to be the focus of forest carbon sink research in the future.\u003c/p\u003e \u003cp\u003eExtreme climatic conditions pose the most significant threat to forest carbon sink\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Drought has caused irreversible carbon losses in numerous large forest ecosystems worldwide\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. We found that the CSI of \u003cem\u003ePinus densata\u003c/em\u003e in Shangri-La decreased in both the 1997\u0026ndash;2002 and 2007\u0026ndash;2012 periods. This is due to Shangri-La experienced severe drought impacts from 1997 to 2002 and again from 2007 to 2012, with precipitation levels lower than historical averages and temperatures exceeding historical norms. Notably, in 2010, a historic drought occurred in Yunnan Province\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, resulting in a substantial decline in precipitation in Shangri-La and a peak in temperatures during the 2007\u0026ndash;2012 period. This contributed to the lowest CSI of \u003cem\u003ePinus densata\u003c/em\u003e during that time.\u003c/p\u003e \u003cp\u003eAlthough there are varying degrees of correlation between individual climate factors and the CSI of \u003cem\u003ePinus densata\u003c/em\u003e in Shangri-La, the influence of climate factors on CSI is often not due to isolated effects but rather the result of synergistic interactions among multiple factors during actual processes. Several scholars\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e have analyzed the driving factors of CS in global terrestrial ecosystem by integrating multiple climate factors and found that most climate factors exhibit synergistic effects, which are significantly influenced by precipitation. This finding aligns with the conclusions of this study. Additionally, in China, the influence of climate factors on forest carbon sink varies regionally\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. For instance, in Southeast China, precipitation has less impact on forest carbon sink compared to temperature\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, while in Northwest China, forest carbon sink show a significant positive correlation in areas of both high and low precipitation, but a significant negative correlation in areas of moderate precipitation\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMoreover, we found that the CSI of \u003cem\u003ePinus densata\u003c/em\u003e is greater in low-temperature environments, which aligns with the observation that photosynthetic rates are higher for this species under such conditions\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Studies indicate that future climatic conditions in plateau are likely to trend toward a \"wet-warm\" climate with high precipitation and elevated temperatures\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In this context, the carbon sink capacity of \u003cem\u003ePinus densata\u003c/em\u003e remains robust. This indicates that this species will continue to play a significant role in carbon reduction and sequestration in the northwestern Yunnan in the future.\u003c/p\u003e \u003cp\u003eThe CSI of \u003cem\u003ePinus densata\u003c/em\u003e in Shangri-La has consistently remained above 10\u0026times; 10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm⁻\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, except during the period from 2007 to 2012, indicating a stable and strong CSI over the years. This finding is consistent with result of Yin et al.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. However, during certain periods, such as 1992\u0026ndash;1997 and 2007\u0026ndash;2012, there was a slight decline in CSI. This decrease can be attributed to significant natural disasters during those times, including droughts, landslides, and earthquakes. These disasters led to vegetation damage and a reduction in vegetation cover\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, consequently resulting in decreased carbon sequestration capacity.\u003c/p\u003e \u003cp\u003eIn addition to the influences of the natural factors, the variations in CSI of \u003cem\u003ePinus densata\u003c/em\u003e in the study area are also related to anthropogenic factors. To protect forest resources, a series of ecological construction projects have been implemented in Southwest China, including the \"Grain for Green Program\", \"Natural Shelterbelt Program\", and \"Returning Grazing Land to Grassland\" initiative\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. As a result of these policies, the quality of forest and carbon density have improved, thereby ensuring the stability of forest carbon sink in Shangri-La\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith the development of the socio-economic, the scope and intensity of human activities have continually increased, leading to a declining trend in CSI of \u003cem\u003ePinus densata\u003c/em\u003e in certain areas, such as Jiantang, Hutiaoxia, and Xiaozhongdian. The rapid growth of the tourism industry has stimulated economic expansion, resulting in a pronounced population aggregation effect and a dramatic increase in urban and rural construction land, which has encroached upon substantial agricultural land\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. This alteration in land use patterns has contributed to a decrease in carbon density of \u003cem\u003ePinus densata\u003c/em\u003e. Therefore, the bidirectional impact of anthropogenic factors on the carbon sink capacity of \u003cem\u003ePinus densata\u003c/em\u003e is an important area for future research.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this article, we estimated the CSI of \u003cem\u003ePinus densata\u003c/em\u003e in Shangri-La from 1987 to 2017. We obtained ground data from the National Forest Inventory (NFI) to calculate the variation of AGCS of \u003cem\u003ePinus densata\u003c/em\u003e. We also extracted remote sensing factors from Landsat time series images to calculate their variation. Subsequently, we established dynamic models using RF and GA-RF based on these variations, selecting the optimal model for CSI estimation. We suggest that the GA significantly improves the estimation accuracy of the RF dynamic model, with the GA-RF model based on annual average changes achieving the best fitting, and making it suitable for CSI estimation. Meanwhile, the CSI of \u003cem\u003ePinus densata\u003c/em\u003e in Shangri-La has a CSI between 7.84 and 12.35×10\u003csup\u003e4\u003c/sup\u003e t C·hm\u003csup\u003e− 2\u003c/sup\u003e from 1987 to 2017, exhibiting a fluctuating downward trend, yet it remains at a stable high level over the years. CSI is higher in northern and central Shangri-La. This reflects the fact that high latitude and altitude contribute to the growth of \u003cem\u003ePinus densata\u003c/em\u003e and increase its carbon sink capacity..\u003c/p\u003e \u003cp\u003eWe applied correlation analysis methods to explore climate-driven mechanisms of CSI in \u003cem\u003ePinus densata\u003c/em\u003e forests. The results indicate that precipitation is the main factor contributing to CSI in \u003cem\u003ePinus densata\u003c/em\u003e forests. Temperature and surface solar radiation also had an effect on CSI. Combining the results of multiple analyses, we found that the effect of triple climate factors on CSI is much stronger. The weak drive of the triple factor is the main driver. This suggests that multifactorial synergy is the main driving mechanism. Thus, Quantitative analysis of the synergistic effects of multiple factors on carbon sink should be strengthened in future studies.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy area\u003c/h2\u003e\n\u003cp\u003eShangri-La is located in the northwestern Yunnan Province, at the southern edge of the Tibetan Plateau and the heart of the Hengduan Mountains, where Yunnan, Sichuan, and Tibet converge, covering a total area of 11,613 km\u003csup\u003e\u003cspan\u003e2\u003c/span\u003e\u003c/sup\u003e (Fig. \u003cspan\u003e1\u003c/span\u003e). The climate in this region is influenced by the southwest Indian Ocean monsoon, with the rainy season from June to October and the dry season from November to the following May. Shangri-La's terrain is mainly highland and mountainous, with an average altitude of more than 3,000 m above sea level. There are a total of 43 tree species, including 10 coniferous species and 33 broadleaf species. \u003cem\u003ePinus densata\u003c/em\u003e is one of the dominant tree species in the area, accounting for 16.2% of the forested area\u003csup\u003e\u003cspan\u003e17\u003c/span\u003e\u003c/sup\u003e. The \u003cem\u003ePinus densata\u003c/em\u003e forest of Shangri-La exhibits strong representativeness and significance in vertical distribution. Therefore, this study contributes to understanding the CS potential of \u003cem\u003ePinus densata\u003c/em\u003e in northwestern Yunnan.\u003c/p\u003e\n\u003ch3\u003eCollection and processing of sample plots\u003c/h3\u003e\n\u003cp\u003eWe employed the NFI data as the ground survey data, which is collected in 1987, 1992, 1997, 2002, 2007, 2012 and 2017. The number of sample plots for \u003cem\u003ePinus densata\u003c/em\u003e measured in each year is as follows: 19, 22, 23, 16, 16, 17, and 23, totaling 136 plots, which include some remeasured plots. Each plot measures 28.28 m × 28.28 m and is recorded using the Beijing 54 coordinate system. The data includes average diameter at breast height (DBH), average tree height (H), and the number of \u003cem\u003ePinus densata\u003c/em\u003e. Due to the absence of 7 consecutive repeated measurements in some fixed plots, we selected 22 fixed plots with 7 consecutive repetitions as baseline data\u003csup\u003e\u003cspan\u003e25\u003c/span\u003e\u003c/sup\u003e. Additionally, the distribution data of \u003cem\u003ePinus densata\u003c/em\u003e in Shangri-La was obtained from the China Forest Management Inventory. The distribution range of \u003cem\u003ePinus densata\u003c/em\u003e and the location of sample plots by year are shown in Fig. \u003cspan\u003e11\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eDue to the difficulty in collecting understory vegetation and soil data, sample plots data is limited to aboveground components. Therefore, this study primarily focuses on the aboveground carbon sink. AGB of the sample plots was calculated by the anisotropic growth equation developed from the project team\u003csup\u003e\u003cspan\u003e17\u003c/span\u003e\u003c/sup\u003e. First, we calculated the average AGB using the average tree height and average diameter at breast height (DBH) of the plots, and then computed the total AGB of the plots based on the average AGB and the number of \u003cem\u003ePinus densata\u003c/em\u003e. The AGB equation was as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAGB =\u003c/em\u003e 0.073 × \u003cem\u003eDBH\u003c/em\u003e\u003csup\u003e1.739\u003c/sup\u003e×\u003cem\u003eH\u003c/em\u003e\u003csup\u003e0.880\u003c/sup\u003e× P (1)\u003c/p\u003e\n\u003cp\u003eWhere, \u003cem\u003eAGB\u003c/em\u003e stands for aboveground biomass (t), \u003cem\u003eDBH\u003c/em\u003e stands for diameter at breast height (cm), \u003cem\u003eH\u003c/em\u003e stands for tree height (m), and P is the number of \u003cem\u003ePinus densata\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eWe screened the sample plots data through the calculation results, in which 5 plots with too small AGB (AGB \u0026lt; 1 t·hm\u003csup\u003e− 2\u003c/sup\u003e) were excluded, and then 6 plots with outliers were screened and excluded according to Pauta's criterion, in which a value is considered as an outlier if it exceeds three times the standard deviation of the mean value\u003csup\u003e\u003cspan\u003e25\u003c/span\u003e\u003c/sup\u003e. Finally, we can get 125 plots.\u003c/p\u003e\n\u003cp\u003eThe AGCS of the sample plots was calculated by multiplying the AGB by the carbon content rate. According to the \u003cem\u003eGuidelines for Carbon Stock Measurement in Forest Ecosystems\u003c/em\u003e issued by the State Forestry and Grassland Administration\u003csup\u003e\u003cspan\u003e46\u003c/span\u003e\u003c/sup\u003e, the average carbon content of \u003cem\u003ePinus densata\u003c/em\u003e dry matter was 0.501. The calculation formula was as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eC\u003c/em\u003e \u003csub\u003estock\u003c/sub\u003e \u003cem\u003e=AGB\u003c/em\u003e × C (2)\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003eC\u003c/em\u003e\u003csub\u003estock\u003c/sub\u003e is the \u003cem\u003eAGCS\u003c/em\u003e (t) in \u003cem\u003ePinus densata\u003c/em\u003e forest and C is the carbon content rate.\u003c/p\u003e\n\u003cp\u003eThe change of AGCS from year \u003cem\u003em\u003c/em\u003e to year \u003cem\u003en\u003c/em\u003e of the same plot is the aboveground carbon sink value of the plot during this period\u003csup\u003e\u003cspan\u003e47\u003c/span\u003e\u003c/sup\u003e. The formula is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eC\u003c/em\u003e \u003csub\u003esink\u003c/sub\u003e \u003cem\u003e=C\u003c/em\u003e \u003csub\u003estock,\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e-C\u003c/em\u003e \u003csub\u003estock,\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e (3)\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003eC\u003c/em\u003e\u003csub\u003esink\u003c/sub\u003e is the aboveground carbon sink (t) in \u003cem\u003ePinus densata\u003c/em\u003e forest, and \u003cem\u003en\u003c/em\u003e and \u003cem\u003em\u003c/em\u003e denote the two different years, \u003cem\u003en\u003c/em\u003e \u0026gt; \u003cem\u003em\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe aboveground carbon sink can be divided by the area to obtain the carbon sink intensity (CSI). A positive value indicates a carbon sink, while a negative value signifies a carbon source\u003csup\u003e\u003cspan\u003e48\u003c/span\u003e\u003c/sup\u003e. Considering the significant differences in administrative areas among the townships in Shangri-La and the varying distribution ranges of \u003cem\u003ePinus densata\u003c/em\u003e, we adopted CSI as the standard, so that the carbon sink capacity of \u003cem\u003ePinus densata\u003c/em\u003e in each township can be more objectively reflected.\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eCollection and processing of remote sensing data\u003c/h2\u003e\n \u003cp\u003eWe obtained Landsat 5 TM and Landsat 8 OLI time series images from the Geospatial Data Cloud website (\u003cspan\u003e\u003cspan\u003ehttps://www.gscloud.cn\u003c/span\u003e\u003c/span\u003e), covering a total of 7 time periods and 21 views (Table \u003cspan\u003e4\u003c/span\u003e). The spatial resolution of the images is 30 m. For each year, we selected the three images with the lowest cloud coverage to ensure minimal cloudiness in the chosen images.\u003c/p\u003e\n \u003cp\u003eTo improve the image quality, we pre-processed all the images: firstly, the initial DN (digit number) values were converted to radiometric values using the Radiometric Correction Tool to remove the effects of the sensors\u003csup\u003e49\u003c/sup\u003e; and atmospheric corrections were performed using the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) module\u003csup\u003e50\u003c/sup\u003e. Then, geometric correction was performed on each image with reference to the calibrated SPOT-5 image, the image coordinate system was corrected to the Beijing 1954 coordinate system to eliminate geometric errors, and the SPOT-5 image was re-sampled to a resolution of 30 m×30 m by bilinear interpolation to ensure that the error was less than 1 pixel; finally, terrain correction was performed using the slope matching model\u003csup\u003e51\u003c/sup\u003e. After terrain correction, the differences in radiance values of the images due to terrain relief are eliminated, and the images can better reflect spectral features\u003csup\u003e25\u003c/sup\u003e. Finally, the preprocessed images were stitched together by corresponding years.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Landsat time-series images\u003c/div\u003e\n \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eSensor type\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eData identification\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eStirp/Line\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eAcquisition time\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" rowspan=\"18\"\u003e\n \u003cp\u003eLandsat5 TM\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1987\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51320401987364BKT00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e132/40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e30 December 1987\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1987\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51310411987357BJC01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e131/41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e23 December 1987\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1987\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51320411987364BKT00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e132/41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e30 December 1987\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1992\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51320401991311BKT00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e132/40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e16 November 1991\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1992\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51310411991320BKT00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e131/41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7 November 1991\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1992\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51320411991311BKT00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e132/41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e16 November 1991\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51320401997279BKT00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e132/40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e6 October 1997\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51310411997320BKT01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e131/41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e16 November 1997\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51320411997311BKT00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e132/41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7 November 1997\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51320402002005BJC00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e132/40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5 January 2002\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51310412002302BJC00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e131/41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e29 October 2002\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51320412002005BJC00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e132/41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5 January 2002\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51320402007003BJC01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e132/40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3 January 2007\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51310412007060BJC00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e131/41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1 March 2007\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51320412006288BJC00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e132/41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e15 October 2006\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51320402011014BKT00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e132/40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e14 January 2011\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51310412011007BKT00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e131/41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7 January 2011\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLT51320412011286BKT00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e132/41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e13 October 2011\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eLandsat8 OLI\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLC08_L2SP_132040_20171216_20200902_02_T1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e132/40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e16 December 2017\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLC08_L2SP_131041_20171225_20200902_02_T1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e131/41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e25 December 2017\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLC08_L2SP_132041_20171216_20200902_02_T1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e132/41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e16 December 2017\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eRemote sensing factors information. Where B1 is the blue band, B2 is the green band, B3 is the red band, B4 is the near-infrared band, B5 is the shortwave infrared-1 band, and B7 is the shortwave infrared-2 band.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eFeature types\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eFactor types\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eRemote sensing factors\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTexture features\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGray-level co-occurrence Matrix\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eHomogeneity (HO); Dissimilarity (DI); Mean (ME); Angular second moment (SM); Entropy (EN); Correlation (CC); Variance (VA); Contrast (CO)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eFiltering of probabilistic statistics\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSkewness (SK)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eSpectral features\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGeneral vegetation index factors\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNDVI = (B4 − B3)/(B4 + B3);\u003c/p\u003e\n \u003cp\u003eND32 = (B3 − B2)/(B3 + B2);\u003c/p\u003e\n \u003cp\u003eND54 = (B5 − B4)/(B5 + B4);\u003c/p\u003e\n \u003cp\u003eND53 = (B5 − B3)/(B5 + B3);\u003c/p\u003e\n \u003cp\u003eND57 = (B5 − B7)/(B5 + B7);\u003c/p\u003e\n \u003cp\u003eND452 = (B4 + B5 − B2)/(B4 + B5 + B2);\u003c/p\u003e\n \u003cp\u003eDVI = B4 − B3;\u003c/p\u003e\n \u003cp\u003eRVI = B4/B3;\u003c/p\u003e\n \u003cp\u003eRVI = B4/B3;\u003c/p\u003e\n \u003cp\u003eARVI = (B4 − (2B3 − B1))/(B4 + (2B3 − B1))\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation enhancement factors\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003ePrincipal component analysis (PCA1, PCA2, PCA3PCA4, PCA5, PCA7);\u003c/p\u003e\n \u003cp\u003eVIS123 = B1 + B2 + B3;\u003c/p\u003e\n \u003cp\u003eMID = B5 + B7;\u003c/p\u003e\n \u003cp\u003eAlbedo = B1 + B2 + B3 + B4 + B5 + B7;\u003c/p\u003e\n \u003cp\u003eMID57 = B5 + B7\u003c/p\u003e\n \u003cp\u003eK-T\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple ratio vegetation indices\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eB4/B2、B5/B3、B5/B4、B5/B7、B7/B3、B3/Albedo、B4×B3/B7\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eOriginal band factors\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eB1、B2、B3、B4、B5、B7\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTo obtain the most relevant modelling factors for forest carbon sink, we extracted two types of remote sensing factors based on extensive literature review\u003csup\u003e\u003cspan\u003e25\u003c/span\u003e,\u003cspan\u003e52\u003c/span\u003e\u003c/sup\u003e: spectral feature factors and texture feature factors. In total, 35 spectral factors and 540 texture factors (various types of textures computed from all single bands from 1 to 19 odd windows of each image) were extracted (Table \u003cspan\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eCollection and processing of Meteorological data\u003c/h2\u003e\n \u003cp\u003eThe meteorological data includes the 1 km resolution monthly average precipitation dataset for China (1901–2022), the 1 km resolution monthly average temperature dataset for China (1901–2022), and the regional high-resolution (10 km) surface solar radiation dataset for China (1983–2017). These data were obtained from the National Tibetan Plateau Data Center (\u003cspan\u003e\u003cspan\u003ehttps://data.tpdc.ac.cn\u003c/span\u003e\u003c/span\u003e), with a horizontal accuracy of 30 m and a vertical accuracy of 20 m. To standardize the coordinate system and resolution with other datasets, the meteorological data were projected to the Beijing 1954 coordinate system and then resampled to a resolution of 30 m\u003csup\u003e\u003cspan\u003e53\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eModeling process\u003c/h2\u003e\n \u003cp\u003eWe employed three types of variation to establish the carbon sink model: 5-year interval variation, 10-year interval variation, and annual average variation. The calculation of the variation was based on two-year co-equal plots: we calculated the variation of the AGCS of the sample plots when the data were recorded in both calculation cycles, while the rest of the non-continuous samples we did not calculate\u003csup\u003e\u003cspan\u003e25\u003c/span\u003e\u003c/sup\u003e. In dynamic models, the variation in remote sensing factors was used as the independent variable, and the variation of the AGCS as the dependent variable. The formulas for calculating the variations are as follows:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e∆V = V\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003en\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e - \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e (4)\u003c/p\u003e\n \u003cdiv id=\"Equ1\"\u003e\n \u003cdiv id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:\\varDelta\\:{V}_{a}=\\frac{\\varDelta\\:V}{n-m}$$\u003c/div\u003e\n \u003cdiv\u003e5\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cem\u003e∆V\u003c/em\u003e is the interval variation value, in which the variation is calculated, \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e are the data values of year \u003cem\u003en\u003c/em\u003e and year \u003cem\u003em\u003c/em\u003e, respectively, and \u003cem\u003e∆V\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e is the annual average change value.\u003c/p\u003e\n \u003cp\u003eUsing the above two equations to calculate the AGCS variation and the corresponding remote sensing factors variation of each sample plots. The variation of AGCS was used as the CS value. The shortest time interval for calculating change data is 5 years, while the longest is 30 years. However, there are few continuous sample plots with time intervals of 15 years or more, and the sample size is insufficient to support the experiment.\u003c/p\u003e\n \u003cp\u003eWe processed the 575 extracted remote sensing factors. Pearson correlation analyses were performed after calculating their 5-year interval, 10-year interval, and annual average variation.\u003c/p\u003e\n \u003cp\u003eThe effectiveness of the model during operation is influenced by the number of features. An excessive number of features can slow down model fitting, increase computational workload, and ultimately affect model stability\u003csup\u003e\u003cspan\u003e10\u003c/span\u003e\u003c/sup\u003e. The original features contain some redundant information, which can decrease model accuracy and lead to negative effects such as overfitting\u003csup\u003e\u003cspan\u003e54\u003c/span\u003e\u003c/sup\u003e. Conversely, too few variables can hinder the correct construction of the model. To avoid the risks of overfitting and covariance, we employed a stepwise regression method\u003csup\u003e\u003cspan\u003e10\u003c/span\u003e\u003c/sup\u003e. Following the methods of researchers\u003csup\u003e\u003cspan\u003e25\u003c/span\u003e,\u003cspan\u003e52\u003c/span\u003e\u003c/sup\u003e, we ultimately selected 10 strongly correlated remote sensing factors, all of which exhibited highly significant correlations (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) for modeling.\u003c/p\u003e\n \u003cp\u003eRF is one of the most effective non-parametric regression models\u003csup\u003e\u003cspan\u003e55\u003c/span\u003e\u003c/sup\u003e. Compared to parametric regression methods, this method does not require testing assumptions such as the normality and independence of variables\u003csup\u003e\u003cspan\u003e54\u003c/span\u003e\u003c/sup\u003e. It can avoid overfitting, perform better with outliers, and handle high-dimensional data\u003csup\u003e\u003cspan\u003e56\u003c/span\u003e\u003c/sup\u003e. Additionally, it can assess the importance of each feature in the model. First, we need to adjust the model parameters, which include four key parameters: the maximum number of iterations (n_estimators), the maximum depth of the decision trees (max_depth), the minimum number of samples at leaf nodes (min_samples_leaf), and the minimum number of samples required to split an internal node (min_samples_split).\u003c/p\u003e\n \u003cp\u003eGA is an adaptive heuristic search algorithm that is part of evolutionary algorithms, based on the principles of natural selection and genetics\u003csup\u003e\u003cspan\u003e16\u003c/span\u003e\u003c/sup\u003e. It applies with historical data provided by random searches to guide the search toward regions of the solution space that perform better. Typically, GA is used to generate high-quality solutions for optimization and search problems\u003csup\u003e\u003cspan\u003e55\u003c/span\u003e\u003c/sup\u003e, thereby avoiding the subjective tuning of hyperparameters associated with regular RF models. The main operation process is divided into four steps: iterations, mutation, crossover and selection.\u003c/p\u003e\n \u003cp\u003eCompared with regular RF models, GA assigns a weight vector to all variables in the feature space to evaluate their importance metrics\u003csup\u003e\u003cspan\u003e15\u003c/span\u003e\u003c/sup\u003e. By optimizing the parameter selection of RF using GA, we established the GA-RF models (Fig. \u003cspan\u003e12\u003c/span\u003e). The main parameters of the GA-RF models involve four key components: population, iterations, mutation, and crossover.\u003c/p\u003e\n \u003cp\u003eThe evaluation of model accuracy is divided into two parts: model fitting and model estimation effect test. 80% of the data are randomly selected for model fitting and 20% for model estimation effect test in each modelling. The adopted model accuracy evaluation indexes include the coefficient of determination (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cspan\u003e2\u003c/span\u003e\u003c/sup\u003e), root mean square error (RMSE), relative root mean square error (rRMSE), and prediction accuracy (\u003cem\u003eP\u003c/em\u003e). The calculation formulas are as follows\u003csup\u003e\u003cspan\u003e19\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e\n \u003cdiv id=\"Equ2\"\u003e\n \u003cdiv id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:{R}^{2}=\\frac{\\sum\\:_{i=1}^{n}{(\\widehat{{y}_{i}}-\\stackrel{-}{y})}^{2}}{\\sum\\:_{i=1}^{n}{({y}_{i}-\\stackrel{-}{y})}^{2}}$$\u003c/div\u003e\n \u003cdiv\u003e6\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ3\"\u003e\n \u003cdiv id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\:\\text{R}\\text{M}\\text{S}\\text{E}=\\sqrt{\\frac{\\sum\\:_{i=1}^{n}{({y}_{i}-\\widehat{{y}_{i}})}^{2}}{n}}$$\u003c/div\u003e\n \u003cdiv\u003e7\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ4\"\u003e\n \u003cdiv id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$$\\:\\text{r}\\text{R}\\text{M}\\text{S}\\text{E}=\\frac{\\text{R}\\text{M}\\text{S}\\text{E}}{\\stackrel{-}{y}}\\times\\:100\\%$$\u003c/div\u003e\n \u003cdiv\u003e8\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ5\"\u003e\n \u003cdiv id=\"FileID_Equ5\" name=\"EquationSource\"\u003e$$\\:P=\\frac{1}{n}\\sum\\:_{i=1}^{n}\\left(1-\\left|\\frac{{y}_{i}-\\widehat{{y}_{i}}}{\\widehat{{y}_{i}}}\\right|\\right)\\times\\:100\\%$$\u003c/div\u003e\n \u003cdiv\u003e9\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere, \u003cspan\u003e\u003cspan\u003e\\(\\:{y}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the true value, \u003cspan\u003e\u003cspan\u003e\\(\\:\\widehat{{y}_{i}}\\)\u003c/span\u003e\u003c/span\u003e represents the model regression value,\u003cspan\u003e\u003cspan\u003e\\(\\:\\stackrel{-}{\\:y}\\)\u003c/span\u003e\u003c/span\u003e is the true value mean, and \u003cem\u003en\u003c/em\u003e is the number of samples.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eAnalysis of driving forces\u003c/h2\u003e\n \u003cp\u003eWe used a correlation analysis method to calculate the degree of correlation between MAP, MAT and SSR and forest CSI based at the pixel scale with the following Eq.\u0026nbsp;5\u003csup\u003e7\u003c/sup\u003e:\u003c/p\u003e\n \u003cdiv id=\"Equ6\"\u003e\n \u003cdiv id=\"FileID_Equ6\" name=\"EquationSource\"\u003e$$\\:{R}_{xy}=\\frac{\\sum\\:_{i=1}^{n}\\left[\\left({x}_{i}-\\stackrel{-}{x}\\right)\\left({y}_{i}-\\stackrel{-}{y}\\right)\\right]}{\\sqrt{\\sum\\:_{i=1}^{n}{({x}_{i}-\\stackrel{-}{x})}^{2}}\\times\\:\\sqrt{\\sum\\:_{i=1}^{n}{({y}_{i}-\\stackrel{-}{y})}^{2}}}$$\u003c/div\u003e\n \u003cdiv\u003e10\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003exy\u003c/em\u003e\u003c/sub\u003e represents the correlation coefficient of variables x and y, and its value range is [-1,1]. When the correlation coefficient is less than 0, it is called negative correlation; when it is greater than 0, it is called positive correlation. When it is equal to 0, it is irrelevant. The closer the absolute value is to 1, the stronger the correlation. \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the CSI in the year \u003cem\u003ei\u003c/em\u003e (t C·hm\u003csup\u003e− 2\u003c/sup\u003e), and \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the climatic factor for year \u003cem\u003ei.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eIn order to determine the effect of a single factor on CSI without interference from other factors, we used partial correlation analysis for further study\u003csup\u003e\u003cspan\u003e58\u003c/span\u003e\u003c/sup\u003e. The formula is as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ7\"\u003e\n \u003cdiv id=\"FileID_Equ7\" name=\"EquationSource\"\u003e$$\\:{r}_{x{y}_{1},{y}_{2}}=\\frac{{R}_{x{y}_{1}}-{R}_{xy2}\\times\\:{R}_{{y}_{1}{y}_{2}}}{\\sqrt{(1-{R}_{x{y}_{2}}^{2})(1-{R}_{{y}_{1}{y}_{2}}^{2})}}$$\u003c/div\u003e\n \u003cdiv\u003e11\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere, \u003cspan\u003e\u003cspan\u003e\\(\\:{r}_{x{y}_{1},{y}_{2}}\\)\u003c/span\u003e\u003c/span\u003e represents the first-order partial correlation coefficient between the CSI and the climatic factor \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e excluding the effect of climatic factor \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e. To discuss the partial correlation coefficients between CSI and the three variables, the second-order partial correlation coefficient should be considered with the following formula\u003csup\u003e\u003cspan\u003e5\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e\n \u003cdiv id=\"Equ8\"\u003e\n \u003cdiv id=\"FileID_Equ8\" name=\"EquationSource\"\u003e$$\\:{r}_{x{y}_{1},{y}_{2}{y}_{3}}=\\frac{{r}_{x{y}_{1},{y}_{2}}-{r}_{x{y}_{3},{y}_{2}}\\times\\:{r}_{{{y}_{1}y}_{3,}{y}_{2}}}{\\sqrt{(1-{r}_{x{y}_{3},{y}_{2}}^{2})(1-{r}_{{y}_{1}{y}_{3}{,y}_{2}}^{2})}}$$\u003c/div\u003e\n \u003cdiv\u003e12\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere, \u003cspan\u003e\u003cspan\u003e\\(\\:{r}_{x{y}_{1},{y}_{2}{y}_{3}}\\)\u003c/span\u003e\u003c/span\u003e represents the second-level partial correlation coefficient between CSI and climatic factor \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e excluding the effect of climate factors \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e. For example, the correlation between CSI and MAP after excluding the double effects of MAT and SSR.\u003c/p\u003e\n \u003cp\u003eThe range of partial correlation coefficient is also [-1,1], less than 0 is negative correlation, more than 0 is positive correlation, and the closer the absolute value is to 1, the closer the degree of partial correlation is.\u003c/p\u003e\n \u003cp\u003eThe significance test was performed by T-test with the following formula\u003csup\u003e\u003cspan\u003e59\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e\n \u003cdiv id=\"Equ9\"\u003e\n \u003cdiv id=\"FileID_Equ9\" name=\"EquationSource\"\u003e$$\\:t=\\frac{r\\times\\:\\sqrt{n-m-1}}{\\sqrt{1-{r}^{2}}}$$\u003c/div\u003e\n \u003cdiv\u003e13\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere, \u003cem\u003er\u003c/em\u003e is the partial correlation coefficient, \u003cem\u003en\u003c/em\u003e is the number of samples, 6(six periods), and \u003cem\u003em\u003c/em\u003e is the number of independent variables, 1. In this paper, the level of significance of the t-test results was set at \u003cem\u003eα\u003c/em\u003e = 0.05\u003csup\u003e60\u003c/sup\u003e. The correlation coefficients between CSI and climate factors were classified as highly significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01), significant (0.01 ≤ \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) and non-significant (\u003cem\u003eP\u003c/em\u003e ≥ 0.05) according to the significance level.\u003c/p\u003e\n \u003cp\u003eMulti-correlation coefficient is an indicator that reflects the degree of correlation between a dependent variable and a set of independent variables (two or more). Moreover, it is a measure of the degree of multi-correlation\u003csup\u003e\u003cspan\u003e60\u003c/span\u003e\u003c/sup\u003e. The formula was calculated as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ10\"\u003e\n \u003cdiv id=\"FileID_Equ10\" name=\"EquationSource\"\u003e$$\\:MR=\\sqrt{1-（1-{R}_{x{y}_{1}}^{2}）(1-{r}_{x{y}_{2},{y}_{1}}^{2})(1-{r}_{x{y}_{3},{y}_{1}{y}_{2}}^{2})}$$\u003c/div\u003e\n \u003cdiv\u003e14\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere, \u003cem\u003eMR\u003c/em\u003e is the multi-correlation coefficient between the three climate factors and CSI. Its value range is [0,1], the larger the multi-correlation coefficient, the closer the linear correlation between the variables.\u003c/p\u003e\n \u003cp\u003eThe significance test of the multi-correlation coefficient was performed by F-test method with the following formula\u003csup\u003e\u003cspan\u003e59\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e\n \u003cdiv id=\"Equ11\"\u003e\n \u003cdiv id=\"FileID_Equ11\" name=\"EquationSource\"\u003e$$\\:F=\\frac{MR}{1-MR}\\times\\:\\frac{n-m-1}{m}$$\u003c/div\u003e\n \u003cdiv\u003e15\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eTo accurately investigate the driving mechanisms of climate change on the CSI of \u003cem\u003ePinus densata\u003c/em\u003e in Shangri-La, we referenced the classification methods used by other researches in Southwest China\u003csup\u003e\u003cspan\u003e59\u003c/span\u003e,\u003cspan\u003e60\u003c/span\u003e\u003c/sup\u003e. Subsequently, based on the significance analysis results of partial correlation and multi-correlation, we established the classification criteria for nine climate driving factors (Table \u003cspan\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eRules of climactic driving factors for CSI of \u003cem\u003ePinus desata\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eClimate driving type\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eCriteria for classification\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePartial correlation coefficient\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMulti-correlation coefficient\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003ePR\u003csub\u003eCSI−Pre\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003ePR\u003csub\u003eCSI−Tem\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003ePR\u003csub\u003eCSI−SSR\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMR\u003csub\u003eCSI−Pre+Tem+SSR\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSingle-factor driver\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMAP only\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eF \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMAT only\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eF \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSSR only\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eF \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eDouble-factor driver\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eDouble: MAP, MAT\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eF \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eDouble: MAP, SSR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eF \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eDouble: MAT, SSR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eF \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTriple-factor driver\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eTriple Strong Drive\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eF \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eTriple Weak Drive\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eF \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNon-climatic driver\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eF ≥ 0.05\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Landsat TM and Landsat OLI data are available through https://www.gscloud.cn/ (accessed on 23 October 2024) and The meteorological data are available through https://data.tpdc.ac.cn/home (accessed on 23 October 2024). NFI data presented in this study are available on request from the corresponding author; the data are not publicly available due to the confidentiality of the dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the National Natural Science Foundation of China (No. 32260390); \u0026ldquo;Young Top Talents\u0026rdquo; special project of the high-level talent training support program of Yunnan province, China, in 2020 (No. YNWR-QNBJ-2020-164); Innovation Programs of Southwest Forestry University (Grant No:LXXK-2023Z06).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eK. Y.\u0026nbsp;\u003c/strong\u003econceptualized the study, processed the data, designed the experiment and wrote the original draft.\u003cstrong\u003e\u0026nbsp;K.L.\u003c/strong\u003e processed the data and analyzed results.\u003cstrong\u003e\u0026nbsp;J.Z.\u0026nbsp;\u003c/strong\u003ecollected data, provided resources and edited the manuscript.\u003cstrong\u003e\u0026nbsp;B.Q.\u0026nbsp;\u003c/strong\u003eanalyzed results.\u003cstrong\u003e\u0026nbsp;F.W.\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;Q.X.\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;J.C., Y.H. and J.Y.\u003c/strong\u003e edited the manuscript. All authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u0026nbsp;\u003c/strong\u003eand requests for materials should be addressed to A.A.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReprints and permissions information\u003c/strong\u003e is available at www.nature.com/reprints.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s note\u003c/strong\u003e Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOpen Access\u003c/strong\u003e This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u0026rsquo;s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u0026rsquo;s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSun, X., Wang, G., Huang, M., Chang, R. \u0026amp; Ran, F. Forest biomass carbon stocks and variation in Tibet\u0026rsquo;s carbon-dense forests from 2001 to 2050. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 34687, DOI: https://doi.org/10.1038/srep34687 (2016).\u003c/li\u003e\n\u003cli\u003eCao J., Tian Y., Wang X. \u0026amp; Sun X. 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Vegetation phenology and its response to climate change in the Yellow RiverBasin from 2001 to 2020. \u003cem\u003eActa Ecologica Sinica \u003c/em\u003e\u003cstrong\u003e44\u003c/strong\u003e, 844-857, DOI: https://doi.org/10.20103/j.stxb.202302220311 (2024) \u003c/li\u003e\n\u003cli\u003eXu Y. \u003cem\u003eet al.\u003c/em\u003e Spatio-Temporal Variation of Vegetation Net Ecosystem Productivity and Relative Contribution of Driving Forces in Southwest China from 2000 to 2020. \u003cem\u003eResearch of Environmental Sciences\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 557\u0026ndash;570, DOI: https://doi.org/10.13198/j.issn.1001-6929.2022.12.01 (2023).\u003c/li\u003e\n\u003cli\u003eHe Y. \u003cem\u003eet al.\u003c/em\u003e The EVl trends and driving factors in Red River Basin affected by the \u0026lsquo;corridorbarrier\u0026rsquo; function during 2000-2014. \u003cem\u003eActa Ecologica Sinica\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 2056\u0026ndash;2064, DOI: https://doi.org/10.5846/stxb201703210483 (2018).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Forest aboveground carbon sink, Time series, Dynamic modelling, Genetic Algorithm, Climate change","lastPublishedDoi":"10.21203/rs.3.rs-5315691/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5315691/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurately estimating forest carbon sink and exploring their climate-driven mechanisms are essential for achieving carbon neutrality and sustainable development. Taking \u003cem\u003ePinus densata\u003c/em\u003e in Shangri-La as the research object, we established three Random Forest (RF) dynamic models based on Landsat time series and ground data with 5-year interval variation, 10-year interval variation, and annual average variation. Then, Genetic Algorithm (GA) was applied to optimize the parameters of RF to establish GA-RF dynamic models, and selected the optimal model to estimate the carbon sink intensity (CSI) of \u003cem\u003ePinus densata\u003c/em\u003e. Finally, climate-driven mechanisms were explored by correlation analysis. We found that 1) the GA-RF model based on the annual average variation had the highest accuracy with an \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e of 0.83. 2) The CSI of \u003cem\u003ePinus densata\u003c/em\u003e in Shangri-La was 7.84\u0026ndash;12.35\u0026times;10\u003csup\u003e4\u003c/sup\u003e t C\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e from 1987 to 2017. 3) Precipitation had the greatest effect on CSI. The joint weak drive of CSI by precipitation, temperature and surface solar radiation was the most dominant form of CSI drive for \u003cem\u003ePinus densata\u003c/em\u003e. These results suggest that the GA-RF model can be used for large-scale long-term estimation of above-ground carbon sinks in highland forests. In addition, the precipitation-led multifactorial synergistic driving mechanism will stabilize the carbon sink capacity of \u003cem\u003ePinus densata\u003c/em\u003e in the long term.\u003c/p\u003e","manuscriptTitle":"Estimating forest aboveground carbon sink based on Landsat time-series and its response to climate change","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-22 15:38:23","doi":"10.21203/rs.3.rs-5315691/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-25T05:06:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-21T15:59:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-13T10:07:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304073984284081592941126080137656993657","date":"2024-11-12T09:39:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32847146790748480117805999071582269244","date":"2024-11-12T09:05:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-12T08:55:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-07T07:39:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-07T07:35:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-07T03:48:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-23T04:52:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c6143a7a-8130-416e-b071-f570959eaa9c","owner":[],"postedDate":"November 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-06T16:02:23+00:00","versionOfRecord":{"articleIdentity":"rs-5315691","link":"https://doi.org/10.1038/s41598-024-84258-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-02 15:57:31","publishedOnDateReadable":"January 2nd, 2025"},"versionCreatedAt":"2024-11-22 15:38:23","video":"","vorDoi":"10.1038/s41598-024-84258-7","vorDoiUrl":"https://doi.org/10.1038/s41598-024-84258-7","workflowStages":[]},"version":"v1","identity":"rs-5315691","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5315691","identity":"rs-5315691","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}