Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China

: China is one of the countries with the most abundant bamboo forest resources in the world, and Zhejiang province is among the top-3 Chinese provinces with richest bamboo forests. For rational bamboo forests management, it is of great signiﬁcance to study the spatiotemporal dynamic changes of Aboveground Carbon (AGC) stocks of bamboo forest in Zhejiang. In this study, remote sensing variables, such as spectral, vegetation indices and texture features of bamboo forest in Zhejiang, were extracted from 32 Landsat TM and OLI images got from four different years (2000, 2004, 2008 and 2014). These variables were subsequently selected with stepwise regression method to build an estimation model of AGC of the bamboo forests. The results showed that (1) the accuracy of bamboo forest remote sensing information extracted from the four different years was high with a classiﬁcation accuracy of >76.26% and an accuracy of users of >91.62%. The classiﬁcation area of bamboo forest was highly consistent with the area from forest resource inventory, and the area accuracy was over 96.50%; (2) the estimation model performed well in predicting the AGC in Zhejiang for different years. The correlation coefﬁcient for estimated and measured AGC was between 63% and 72% with low root mean square error; (3) the derived AGC of the bamboo forests in Zhejiang province increased gradually from 2000 to 2014, with the AGC density of 6.75 Mg · ha − 1 , 10.95 Mg · ha − 1 , 15.25 Mg · ha − 1 and 19.07 Mg · ha − 1 respectively, and the average annual growth of 0.88 Mg · ha − 1 . The spatiotemporal evolution of bamboo forest AGC in Zhejiang province had a close relationship with the gradual expansion of bamboo forest in the province and the differentiation of management levels in different regions.


Introduction
Bamboos are naturally distributed in the tropical, subtropical and temperate regions of all the continents except Europe and western Asia, from lowland up to 4000 m in altitude [1], and mainly in Asia [2][3][4][5][6][7].China locates in the center of world's bamboo distribution area, and is the most important bamboo industry country in the world.Bamboo area in China reached 6.01 million hectares, accounting for approximately 20% of the total area of bamboo forests in the world [8].Zhejiang, locating in the middle-east of China, has more than 0.9 million hectares of bamboo forest, being one of the top-3 Chinese provinces with richest bamboo forest resource.Such abundant bamboo forest resources in Zhejiang is mainly attributed to high level of management and developed bamboo industry.Therefore, it is said that "World bamboo forests focus on China, and Chinese bamboo forests focus on Zhejiang".
Remote sensing data has been widely used for estimates of AGC in forests [27][28][29][30][31], and in recent years, high-resolution satellite imageries were used due to the development of technologies [32][33][34][35][36][37][38][39], e.g., the multi-resolution remote sensing imagery satellites [40][41][42][43][44][45].Although the application of high spatial and spectral resolution sensors succeed in AGC estimation, there are still limitations such as high acquisition costs, small area coverage, multicollinearity, limited availability and a narrow bandwidth [46].The above limitations lead to more workload and lower efficiency in estimating AGC of large-scale forest.Researches of the spatiotemporal evolution and the carbon storage estimation of bamboo forest by using medium-resolution remote sensing data has achieved plenteous results [47][48][49][50][51][52].Meanwhile, algorithms for estimating carbon stocks/biomass based on remote sensing data have also been developed.In early studies, carbon stocks/biomass estimation was mostly conducted with linear or nonlinear regression models.For example, Anaya et al. [53] constructed a linear model using enhanced vegetation index (EVI) and related characteristics to estimate AGC in different vegetation types.Du et al. [49] used Partial Least Squares (PLS) method to estimate the carbon storage of bamboo forest in Anji County.Xu et al. [54] estimated the carbon storage of Phyllostachys praecox using the PLS regression.In recent years, machine learning algorithms have been widely used not only for inversion of land use changes but also for carbon storage estimation.Zhou et al. [26] used the K-Nearest Neighbor (KNN) method to estimate carbon storage in bamboo forests.Vafaei et al. [41] used Random Forests (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MPL Neural Net) and Gaussian Processes (GP) methods to estimate the forest AGC, and proved high accuracy of the methods.Gao et al. [55] combined Artificial Neural Network (ANN), SVR, RF, KNN, and Linear Regression (LR) methods to estimate forest AGC.
However, the previous studies mainly focused on small-scale area e.g., protected areas and countries.Estimation of spatiotemporal variation of bamboo forest carbon stocks on a large scale is of great significance for understanding the function of bamboo forest on climate change.In this study, we took Zhejiang province as a case study to establish AGC models of bamboo forest using the remote sensing information from Landsat5 TM and Landsat8 OLI data.The spatiotemporal dynamics of bamboo AGC was estimated and analyzed based on the extraction of spatial and temporal distribution of bamboo forests in four years (2000, 2004, 2008 and 2014) in Zhejiang Province.The study aimed at providing an insight into the spatiotemporal dynamics of bamboo forest carbon stocks in a long-time series at a national or global scale.

Study Area
Zhejiang province (Figure 1) is located in south of Yangtze River Delta in southeast coast, China (118-123 • E and 27-31 • N).Under subtropical monsoon climate, it has clearly demarcated seasons, suitable temperature with abundant rainfall.The annual mean temperature for Zhejiang is between 15 • C to 18 • C, and the annual precipitation varies in a range of 980-2000 mm.Zhejiang province has a rich forest resource, covering approximately 6.06 million hectares of forest land, 0.9 million hectares

Dataset and Landsat TM Image Preprocessing
In this study, we selected 8 scene remote sensing images per year (2000, 2004, 2008 and 2014), and each of these 32 images covered the whole province (Table ??).In order to reduce the influence of acquisition time, vegetation spectral reflectance, cloud thickness and other factors on classification results, we selected the images with cloud cover below 10%, smallest observational zenith angle, and consistent time interval.We used the FLAASH method to make atmospheric correction for each image, with consideration of eliminating two major kinds of influence factors: (1) those such as water vapor, aerosol, bidirectional reflection and data transmission, which may influence the trend analysis and information extraction in time series [58,59]; and (2) the radiation difference between multi-temporal remote sensing data.Then, the corrected images were furtherly geometrically corrected using ground control points (GCPs) to splice the remote sensing data of Zhejiang Province [60].

Mapping of Bamboo
In this study, land use was classified into six types: bamboo, broad-leaved forest, coniferous forest, farmland, barren land, and water bodies [61].Based on the fifth to eighth forest resource inventories (1994-1998, 1999-2003, 2004-2008 and 2009-2013) in Zhejiang Province, the spectral reflectance characteristics of samples were used to select Regions of Interest (ROIs) as the training samples for maximum likelihood classification based on visual interpretation, and the sampling data that derived from the continuous forest resource inventory data of Zhejiang Province, were used to validate the classification results.Table ?? shows the numbers of validation samples for bamboo, broad-leaved forest, coniferous forest, farmland, barren land and water bodies indifferent years.

AGC Estimation
As bamboo forest was dominated by Moso bamboo in Zhejiang province, the Moso bamboo sample plots were used for spatiotemporal estimation of carbon stocks on bamboo forest from 2000 to 2014 in this study.The numbers of bamboo forest plots were 137, 189, 203 and 139, respectively (Figure 1).Above Ground Biomass (AGB) of individual Moso bamboo was calculated based on the Equation (1) [62]: M(D, A) = 747.787D 2.771 0.148A 0.028 + A 5.555 where M, D, and A denote AGB (dry weight in Kg), DBH (cm), and age (du), respectively.For each plot, the AGB is a sum of all individual Moso bamboo AGB within the plot, and the expansion factor for the conversion from biomass to carbon for Moso bamboo forestis 0.5042 [9].

Setting of Remote Sensing Variables
The variables of bamboo forest AGC model consisted of three types, i.e., original band combinations, vegetation indices and band texture (Table 1).Five different window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9, and 11 × 11) were set for calculation of texture variables.Due to different numbers between Landsat5 and Landsat8 bands, the number of variables was 250 in 2000, 2004 and 2008, while 290 in 2014.When the sample plot coordinates were matched with the pixel values of remote sensing variables, they might not fully matched with each other due to geometric correction and positioning error.In order to reduce the matching error, a window size of 3 × 3 pixels was used to extract the mean values of the selected remote sensing variables for each plot [63][64][65][66].

Method of Model Construction
Stepwise regression screening variables method, one of the most widely used methods in regression models [67,68], was used to establish a remote sensing information model of AGC in bamboo forests.70% of the sample plots were randomly selected for developing the model, and the others for evaluating the established model.AGC may have high or weak relationships with remote sensing variables.Because of the strong correlations among some explanatory variables, it was critical to eliminate the variables that have a high correlation between themselves and nonsignificant correlations between variables and AGC [55,69].The advantage of stepwise regression is to determine the importance of explanatory variables and eliminate the influence of collinearity on accuracy of models.Correlation analysis can be used to examine the relationship between AGB and remote sensing variables.The basic idea of stepwise regression is to introduce the variables one by one into the model.After each of the explanatory variables was introduced, the F-test must be conducted and the explanatory variables that have been selected must be t-test one by one.When the originally introduced explanatory variable becomes less significant due to the introduction of later explanatory variables, it was deleted to ensure that the regression equation contained only significant variables before each new variable was introduced.The process did not stopped until there was no significant explanatory variable to choose the regression equation, and no significant explanatory variables were excluded from the regression equation.After applying stepwise regression, the obtained explanatory variables were optimal, and there was no serious collinearity among variables.
V(i, j) is the ith row of the jth column in the Nth moving window;

Model Evaluation
The model evaluation indexes mainly included Relative Error (RE), Mean Relative Error (MRE) and root mean square error (RMSE), as well as the analysis and evaluation of the extreme values of the model predictive value.Formulas were listed as follows, where i represents the ith sample: where Obs_AGC i represent the observed AGC of the ith sample, Pre_AGC i represent the predicted AGC of the ith sample, n represent the number of sample, respectively.

Spatiotemporal Distribution of Bamboo
The accuracies of bamboo forest remote sensing information extraction in 2000, 2004, 2008 and 2014 are shown in Table 2. Table 2 shows that the overall accuracy of land use types at different times is above 76.26%, kappa coefficient is higher than 0.75.The overall classification accuracy is high.Producer's accuracy of bamboo forest is above 75.86%,and the user's accuracy is above 91.62%.In addition, the accuracy of area extraction is over 96.50%, which is a satisfying result according to the actual area of the forest management inventory.Figure 2 shows the spatial distribution of bamboo forests in Zhejiang Province.According to Figure 2, the area of bamboo forests in Zhejiang Province showed a gradually increasing trend in time and space from the year of 2000 to 2014.
Based on the time-series Landsat data, the distribution information of bamboo forests in Zhejiang Province was extracted, which exhibited high accuracies in terms of both classification results and area statistics.The spatiotemporal distribution characteristics of bamboo forests are consistent with the actual situation, which provide a more accurate data.

Model Evaluation
The model evaluation indexes mainly included Relative Error (RE), Mean Relative Error (MRE) and root mean square error (RMSE), as well as the analysis and evaluation of the extreme values of the model predictive value.Formulas were listed as follows, where i represents the ith sample: where i Obs_AGC represent the observed AGC of the ith sample, i Pre_AGC represent the predicted AGC of the ith sample, n represent the number of sample, respectively.

Spatiotemporal Distribution of Bamboo
The accuracies of bamboo forest remote sensing information extraction in 2000, 2004, 2008 and 2014 are shown in Table 4. Table 4 shows that the overall accuracy of land use types at different times is above 76.26%, kappa coefficient is higher than 0.75.The overall classification accuracy is high.Producer's accuracy of bamboo forest is above 75.86%,and the user's accuracy is above 91.62%.In addition, the accuracy of area extraction is over 96.50%, which is a satisfying result according to the actual area of the forest management inventory.Figure 2 shows the spatial distribution of bamboo forests in Zhejiang Province.According to Figure 2, the area of bamboo forests in Zhejiang Province showed a gradually increasing trend in time and space from the year of 2000 to 2014.
Based on the time-series Landsat data, the distribution information of bamboo forests in Zhejiang Province was extracted, which exhibited high accuracies in terms of both classification results and area statistics.The spatiotemporal distribution characteristics of bamboo forests are consistent with the actual situation, which provide a more accurate data.

Accuracy Assessment of AGC Model
Figure 3 shows the correlation between the predicted AGC and the observed AGC for the model.All of the correlation coefficients R of training and testing data values range from 0.63 to 0.72 in different years (Figure 3; p < 0.01), and Mean Relative Error (MRE) is less than 0.377, and the highest accuracy of the AGB model was achieved in the year of 2014 (R = 0.72, RMSE = 2.9, MRE = 0.314).Both the model accuracy and the verifying accuracy pointed to good performance of the models.Further analysis were statistics of model predicting ability (Table 5).According to the model predicted value, (1) the maximum, minimum and average value of model predicted value exhibited increasing trends over time from 2000 to 2014.As the economic benefit increasing of bamboo forest, and the management level of bamboo forests in Zhejiang Province has been improved year by year.Meanwhile, the biomass has also increased [70,71].Therefore, the predicted value of AGC maximum, minimum and mean value are consistent with the actual situation; (2) Predicted STD and MRE value of bamboo forest AGC are relatively stable from 2.2 to 2.9, which indicates that the model is stable and has good performance in predicting the spatiotemporal distribution of AGC in bamboo forest.

AGC Spatiotemporal Evolution of Bamboo Forest
According to the AGC model of bamboo forest in Zhejiang Province, the spatial distribution of AGC in different periods from 2000 to 2014 is shown in Figure 4.As is known, the carbon density of bamboo Zhejiang province has been continuously increasing over time, especially in Hangzhou, Shaoxing, Quzhou, Ningbo, Lishui, Jinhua, and Wenzhou.Further analysis were statistics of model predicting ability (Table 3).According to the model predicted value, (1) the maximum, minimum and average value of model predicted value exhibited increasing trends over time from 2000 to 2014.As the economic benefit increasing of bamboo forest, and the management level of bamboo forests in Zhejiang Province has been improved year by year.Meanwhile, the biomass has also increased [70,71].Therefore, the predicted value of AGC maximum, minimum and mean value are consistent with the actual situation; (2) Predicted STD and MRE value of bamboo forest AGC are relatively stable from 2.2 to 2.9, which indicates that the model is stable and has good performance in predicting the spatiotemporal distribution of AGC in bamboo forest.

AGC Spatiotemporal Evolution of Bamboo Forest
According to the AGC model of bamboo forest in Zhejiang Province, the spatial distribution of AGC in different periods from 2000 to 2014 is shown in Figure 4.As is known, the carbon density of bamboo Zhejiang province has been continuously increasing over time, especially in Hangzhou, Shaoxing, Quzhou, Ningbo, Lishui, Jinhua, and Wenzhou.From the spatiotemporal distribution of bamboo forest carbon stock in 11 cities, the cities with the highest and lowest increase in total carbon storage in four periods are Hangzhou and Jiaxing.The carbon storage in Hangzhou City increased from 0.81 Tg C in 2000 to 3.47 Tg C in 2014, more than quadrupled with an average annual increase of 0.19 Tg C; Jiaxing City has low carbon storage because of the small bamboo area; otherwise, Zhoushan City has maintained a relatively stable total carbon stock.At the same time, the carbon density of bamboo forests in all counties and cities maintained a continuous growth pattern at different periods (Table 6), but the difference in growth ranged greatly.Among them, the growth of carbon stock density in Huzhou and Hangzhou was the most obvious, from 7.99 And 7.62 Mg•ha The role of the predictive variable for the AGC estimation in this study of different years was assessed using factor analysis method in the SPSS.20 software (Statistical Product and Service Solutions, SPSS; International Business Machines Corporation, IBM; Chicago, America).Accordingly, the correlation coefficient was used to calculate the merit of the variables.The result is listed in Table 7.It can be seen that W7B3Con, SAVI, W7B2Con, and NDVI were the most important variables for From the spatiotemporal distribution of bamboo forest carbon stock in 11 cities, the cities with the highest and lowest increase in total carbon storage in four periods are Hangzhou and Jiaxing.The carbon storage in Hangzhou City increased from 0.81 Tg C in 2000 to 3.47 Tg C in 2014, more than quadrupled with an average annual increase of 0.19 Tg C; Jiaxing City has low carbon storage because of the small bamboo area; otherwise, Zhoushan City has maintained a relatively stable total carbon stock.At the same time, the carbon density of bamboo forests in all counties and cities maintained a continuous growth pattern at different periods (Table 4), but the difference in growth ranged greatly.Among them, the growth of carbon stock density in Huzhou and Hangzhou was the most obvious, from 7.99 And 7.62 Mg•ha predicting the bamboo forest AGC in different years.Meanwhile, we could realize that the most important variables in different years were derived from vegetable indexes and texture, and they could improve the accuracy.The results are inconsistent with the recent study reported by Vafaei et al. [41] and Eckert [37].predicting the bamboo forest AGC in different years.Meanwhile, we could realize that the most important variables in different years were derived from vegetable indexes and texture, and they could improve the accuracy.The results are inconsistent with the recent study reported by Vafaei et al. [41] and Eckert [37].

Discussion
The study shows that the stepwise regression method of Zhejiang province AGC spatiotemporal estimation of remote sensing information model has a good performance.The predicting ability of the model is strong.Errors such as RMSE and predicted error are small.Figure 5 shows the analysis of the residual distribution of the model prediction residual error when the standardization residual threshold of the test sample was 2. As is shown in Figure 9, the STD of all test samples were within the threshold range, which further illustrated that the model had good stability and reliability in predicting AGC of bamboo forests at the provincial scale.
The results shows that the bamboo forest AGC and carbon density both had an increasing trend from 2000 to 2014 in Zhejiang province.The study has great consistency with the previous researches by plot sampling [9] or ecological process model simulations [72] (Table 8, Except for the lack of data on related studies in 2000).Certainly, there are some discrepancies mainly coursed by different methods or models of estimation.The remote sensing information model is a kind of spatial information model [73].There are some differences on spectrum, texture, and vegetation index in a The role of the predictive variable for the AGC estimation in this study of different years was assessed using factor analysis method in the SPSS.20 software (Statistical Product and Service Solutions, SPSS; International Business Machines Corporation, IBM; Chicago, America).Accordingly, the correlation coefficient was used to calculate the merit of the variables.The result is listed in Table 5.It can be seen that W 7 B 3 Con, SAVI, W 7 B 2 Con, and NDVI were the most important variables for predicting the bamboo forest AGC in different years.Meanwhile, we could realize that the most important variables in different years were derived from vegetable indexes and texture, and they could improve the accuracy.The results are inconsistent with the recent study reported by Vafaei et al. [41] and Eckert [37].

Discussion
The study shows that the stepwise regression method of Zhejiang province AGC spatiotemporal estimation of remote sensing information model has a good performance.The predicting ability of the model is strong.Errors such as RMSE and predicted error are small.Figure 5 shows the analysis of the residual distribution of the model prediction residual error when the standardization residual threshold of the test sample was 2. As is shown in Figure 9, the STD of all test samples were within the threshold range, which further illustrated that the model had good stability and reliability in predicting AGC of bamboo forests at the provincial scale.
The results shows that the bamboo forest AGC and carbon density both had an increasing trend from 2000 to 2014 in Zhejiang province.The study has great consistency with the previous researches by plot sampling [9] or ecological process model simulations [72] (Table 6, Except for the lack of data on related studies in 2000).Certainly, there are some discrepancies mainly coursed by different methods or models of estimation.The remote sensing information model is a kind of spatial information model [73].There are some differences on spectrum, texture, and vegetation index in a same background object that may influence the estimation results; however, the remote sensing has obvious advantages in large-scale dynamic monitoring.The results of this study and previous studies provide a guarantee for using the remote sensing information to accurately monitor the spatiotemporal dynamics of bamboo carbon storage in a wide range.same background object that may influence the estimation results; however, the remote sensing has obvious advantages in large-scale dynamic monitoring.The results of this study and previous studies provide a guarantee for using the remote sensing information to accurately monitor the spatiotemporal dynamics of bamboo carbon storage in a wide range.The previous researches showed that there was obvious relationship between the trend of gradual increase of bamboo forest carbon stocks and carbon density, bamboo forest area gradual expansion and improving management level of Zhejiang province [51,71].Based on Table 6, Figure 10 showed the relationship between spatiotemporal changes in carbon stocks and carbon densities and bamboo forest area in 2000-2004, 2004-2008, 2014-2008 and 2000-2014 in different counties and cities in Zhejiang Province.As we can see from Figure 6, there is a significant linear correlation between the change of bamboo forest carbon storage and bamboo forest area.The main reason why there was no obvious linear relationship between the change of carbon density and the area of bamboo forest was that average carbon storage per unit area was affected by management level.As is shown in Table 6, the general change of bamboo forest area in Huzhou was small, especially the year of 2008-2014, but the carbon density of bamboo forest remained relatively high, and the highest of 2014 was 22.44 Mg C/ha, which was related to the long-term intensive management in the region and the high biomass of bamboo forests [75][76][77][78].Therefore, the management level of bamboo forests in different regions will also affect the spatial and temporal evolution of carbon storage to a huge extent.
In this paper, the estimation accuracy R of carbon storage models for different years range from0.63 to 0.69 compared with previous studies.For instance, Shang et al. [22] used MODIS images  The previous researches showed that there was obvious relationship between the trend of gradual increase of bamboo forest carbon stocks and carbon density, bamboo forest area gradual expansion and improving management level of Zhejiang province [51,71].Based on Table 4, Figure 10 showed the relationship between spatiotemporal changes in carbon stocks and carbon densities and bamboo forest area in 2000-2004, 2004-2008, 2014-2008 and 2000-2014 in different counties and cities in Zhejiang Province.As we can see from Figure 10, there is a significant linear correlation between the change of bamboo forest carbon storage and bamboo forest area.The main reason why there was no obvious linear relationship between the change of carbon density and the area of bamboo forest was that average carbon storage per unit area was affected by management level.As is shown in Table 4, the general change of bamboo forest area in Huzhou was small, especially the year of 2008-2014, but the carbon density of bamboo forest remained relatively high, and the highest of 2014 was 22.44 Mg C/ha, which was related to the long-term intensive management in the region and the high biomass of bamboo forests [75][76][77][78].Therefore, the management level of bamboo forests in different regions will also affect the spatial and temporal evolution of carbon storage to a huge extent.
In this paper, the estimation accuracy R of carbon storage models for different years range from 0.63 to 0.69 compared with previous studies.For instance, Shang et al. [22] used MODIS images to estimate bamboo forest AGC and reported an R of 0.68.Shang et al. [47] estimated the AGC of Moso bamboo forests in combination with Landsat and MODIS data, and the estimation accuracy R was 0.70.Zhu et al. [79] estimated forest biomass using time series Landsat data and the model accuracy was 0.69.Sandra Eckert [37] used WorldView-2 data estimated forest AGB and model accuracy R is up to 0.93.Vafaei et al. [41] used ALOS-2 PALSAR-2 and Sentinel-2A data to estimate above-ground biomass in forests, with a highest estimated R = 0.85.The estimation accuracy in this study is similar to those studies based on medium resolution images, and it was less accurate than models based on high-resolution images.In this research, the satellite remote sensing image used to estimate the carbon stocks were Landsat time series of medium resolution data with a spatial resolution of 30 × 30 m, and there were many disadvantages compared to the high-resolution satellite data used to estimate forest AGC [80].Previous studies showed that the combination with multi-source remote sensing data could effectively improve the estimation accuracy of AGC [81].Remote sensing data were affected by their own spectral resolution, resulting in differences in the extraction accuracy of the band spectrum, vegetation index, texture information compared with high-resolution images [37,38,42,81].In addition, due to the large span of date and time acquisition of the eight scenes in the same period, although atmospheric correction was performed, radiation differences could not be completely eliminated, resulting in errors in bamboo forest information extraction [82].This is what we might improve in the future study.In this research, the satellite remote sensing image used to estimate the carbon stocks were Landsat time series of medium resolution data with a spatial resolution of 30 × 30 m, and there were many disadvantages compared to the high-resolution satellite data used to estimate forest AGC [80].Previous studies showed that the combination with multisource remote sensing data could effectively improve the estimation accuracy of AGC [81].Remote sensing data were affected by their own spectral resolution, resulting in differences in the extraction accuracy of the band spectrum, vegetation index, texture information compared with high-resolution images [37,38,42,81].In addition, due to the large span of date and time acquisition of the eight scenes in the same period, although atmospheric correction was performed, radiation differences could not be completely eliminated, resulting in errors in bamboo forest information extraction [82].This is what we might improve in the future study.

Conclusions
Based on the time series imageries of Landsat 5 TM and Landsat 8 OLI in 2000, 2004, 2008 and 2014, we took Zhejiang province as study area, as a precondition to extract bamboo forest distribution information in different periods of Zhejiang province.Model of remote sensing variables were constructed to estimate the spatiotemporal evolution of bamboo forest AGC in Zhejiang province.The results shows that: (1) The spatiotemporal distribution of bamboo forests in Zhejiang Province at different periods had a higher accuracy of information extraction, of which the classification accuracy reached above 76.36%, the user's accuracy was above 91.62% and the area accuracy was above 96.50%.( 2

Figure 1 .
Figure 1.Study area and location of bamboo forest carbon storage sample plots.

Figure 1 .
Figure 1.Study area and location of bamboo forest carbon storage sample plots.

Figure 2 .
Figure 2. The bamboo forest information extraction in different years throughout Zhejiang Province.

Figure 2 .
Figure 2. The bamboo forest information extraction in different years throughout Zhejiang Province.

Figure 3
Figure3shows the correlation between the predicted AGC and the observed AGC for the model.All of the correlation coefficients R of training and testing data values range from 0.63 to 0.72 in different years (Figure3; p < 0.01), and Mean Relative Error (MRE) is less than 0.377, and the highest accuracy of the AGB model was achieved in the year of 2014 (R = 0.72, RMSE = 2.9, MRE = 0.314).Both the model accuracy and the verifying accuracy pointed to good performance of the models.

Figure 3 .
Figure 3. Relationship between Observed AGC and Predicted AGC in different years (significance at the 0.01 level).

Figure 3 .
Figure 3. Relationship between Observed AGC and Predicted AGC in different years (significance at the 0.01 level).

Figure 4 .
Figure 4.The AGC spatial Distribution map of bamboo forest in Zhejiang Province in different years.

Figure 4 .
Figure 4.The AGC spatial Distribution map of bamboo forest in Zhejiang Province in different years.

Figure 9 .
Figure 9.The test sample standardized residual in different years.

Figure 9 .
Figure 9.The test sample standardized residual in different years.
Remote Sens. 2018, 10, x FOR PEER REVIEW 18 of 23 Moso bamboo forests in combination with Landsat and MODIS data, and the estimation accuracy R was 0.70.Zhu et al. [79] estimated forest biomass using time series Landsat data and the model accuracy was 0.69.Sandra Eckert [37] used WorldView-2 data estimated forest AGB and model accuracy R is up to 0.93.Vafaei et al. [41] used ALOS-2 PALSAR-2 and Sentinel-2A data to estimate above-ground biomass in forests, with a highest estimated accuracy R = 0.85.The estimation accuracy in this study is similar to those studies based on medium resolution images, and it was less accurate than models based on high-resolution images.

Figure 10 .
Figure 10.Relationship between bamboo forest area and carbon density, total carbon storage in different years.(a-d) represents the year of 2000, 2004, 2008 and 2014, respectively.

Figure 10 .
Figure 10.Relationship between bamboo forest area and carbon density, total carbon storage in different years.(a-d) represents the year of 2000, 2004, 2008 and 2014, respectively.

Table 1 .
Information of remote sensing variables.

Table 4 .
Accuracy of classification and bamboo forest extraction in Zhejiang Province.

Overall Accuracy of Land Use Classification Bamboo Forest Classification Accuracy Bamboo Area Estimation Accuracy Accuracy (%) Kappa Coefficient Producer
's Accuracy (%) User's Accuracy (%)

Table 2 .
Accuracy of classification and bamboo forest extraction in Zhejiang Province.

Table 5 .
Statistical analysis of the model prediction and residual.
Table 6 shows the statistical results of bamboo forest AGC at different periods in 11 cities of Zhejiang.According to the statistics, the carbon density in Zhejiang Province increased from 6.75 Mg•ha −1 in 2000 to 19.07 Mg•ha −1 in 2014, with a growth rate of 182.52%.Carbon storage increased from 5.14 Tg in 2000 to 16.94 Tg in 2014.

Table 3 .
Statistical analysis of the model prediction and residual.
Table 4 shows the statistical results of bamboo forest AGC at different periods in 11 cities of Zhejiang.According to the statistics, the carbon density in Zhejiang Province increased from 6.75 Mg•ha −1 in 2000 to 19.07 Mg•ha −1 in 2014, with a growth rate of 182.52%.Carbon storage increased from 5.14 Tg in 2000 to 16.94 Tg in 2014.

Table 4 .
The Spatial and Temporal Changes of Aboveground Carbon Storage in Bamboo Forest from 2000 to 2014 in Zhejiang Province.

Table 7 .
The importance of the variables for the AGC estimation in different years.

Table 5 .
The importance of the variables for the AGC estimation in different years.

Table 8 .
The results were compared on the AGC of Bamboo Forest in Zhejiang Province.

Table 6 .
The results were compared on the AGC of Bamboo Forest in Zhejiang Province.

Area Year Carbon Density (Mg•ha −1 ) Carbon Stock (Tg C) The Study References The Study References
) Bamboo forest AGC spatiotemporal estimation model built by the stepwise regression method in Zhejiang Province has good performance and robustness.RMSE and prediction error are small.The estimated carbon storage results have a good consistency with the previous research.(3) Bamboo forest AGC storage shows gradually increased tend in Zhejiang province from 2000 to 2014, and the average carbon stock density at different years was 6.75 Mg•ha −1 , 10.95 Mg•ha −1 , 15.25 Mg•ha −1 and 19.07 Mg•ha −1 , and an average annual growth was 0.88 Mg•ha −1 .Spatiotemporal evolution of bamboo forest carbon stocks has close relationships with the expansion of bamboo forest area and the differences in management level in various regions of Zhejiang province.