Spatiotemporal dynamic of subtropical forest carbon storage and its resistance and resilience to drought in China

Subtropical forests are rich in vegetation and have high photosynthetic capacity. China is an important area for the distribution of subtropical forests, evergreen broadleaf forests (EBFs) and evergreen needleleaf forests (ENFs) are two typical vegetation types in subtropical China. Forest carbon storage is an important indicator for measuring the basic characteristics of forest ecosystems and is of great significance for maintaining the global carbon balance. Drought can affect forest activity and may even lead to forest death and the stability characteristics of different forest ecosystems varied after drought events. Therefore, this study used meteorological data to simulate the standardized precipitation evapotranspiration index (SPEI) and the Biome-BGC model to simulate two types of forest carbon storage to quantify the resistance and resilience of EBF and ENF to drought in the subtropical region of China. The results show that: 1) from 1952 to 2019, the interannual drought in subtropical China showed an increasing trend, with five extreme droughts recorded, of which 2011 was the most severe one; 2) the simulated average carbon storage of the EBF and ENF during 1985-2019 were 130.58 t·hm-2 and 78.49 t·hm-2, respectively. The regions with higher carbon storage of EBF were mainly concentrated in central and southeastern subtropics, where those of ENF mainly distributed in the western subtropic; 3) The median of resistance of EBF was three times higher than that of ENF, indicating the EBF have stronger resistance to extreme drought than ENF. Moreover, the resilience of two typical forest to 2011 extreme drought and the continuous drought events during 2009 - 2011 were similar. The results provided a scientific basis for the response of subtropical forests to drought, and indicating that improve stand quality or expand the plantation of EBF may enhance the resistance to drought in subtropical China, which provided certain reference for forest protection and management under the increasing frequency of drought events in the future

Soil respiration of a Moso bamboo forest significantly affected by gross ecosystem productivity and leaf area index in an extreme drought event

Moso bamboo has large potential to alleviate global warming through carbon sequestration. Since soil respiration (R-s) is a major source of CO2 emissions, we analyzed the dynamics of soil respiration (R-s) and its relation to environmental factors in a Moso bamboo (Phllostachys heterocycla cv. pubescens) forest to identify the relative importance of biotic and abiotic drivers of respiration. Annual average R(s )was 44.07 t CO2 ha(-1) a(-1) R-s correlated significantly with soil temperature (P <0.01), which explained 69.7% of the variation in R-s at a diurnal scale. Soil moisture was correlated significantly with R-s on a daily scale except not during winter, indicating it affected R-s. A model including both soil temperature and soil moisture explained 93.6% of seasonal variations in R-s. The relationship between R-s and soil temperature during a day showed a clear hysteresis. R-s was significantly and positively (P <0.01) related to gross ecosystem productivity and leaf area index, demonstrating the significance of biotic factors as crucial drivers of R-s.Peer reviewe

Wavelet Vegetation Index to Improve the Inversion Accuracy of Leaf V²⁵_cmax of Bamboo Forests

Maximum carboxylation rate (Vcmax) is a key parameter to characterize the forest carbon cycle process. Hence, monitoring the Vcmax of different forest types is a hot research topic at home and abroad, and hyperspectral remote sensing is an important technique for Vcmax inversion. Moso bamboo is a unique forest type with a high carbon sequestration capacity in subtropical regions, but the lack of Vcmax data is a major limitation to accurately modeling carbon cycling processes in moso bamboo forests. Our study area was selected in the moso bamboo forest carbon sink research base in Shanchuan Township, Anji County, Zhejiang Province, China, which has a typical subtropical climate and is widely distributed with moso bamboo forests. In this study, the hyperspectral reflectance and V25cmax (Vcmax converted to 25 °C) of leaves of newborn moso bamboo (I du bamboo) and 2- to 3-year-old moso bamboo (II du bamboo) were measured at different canopy positions, i.e., the top, middle and bottom, in the moso bamboo forest. Then, we applied a discrete wavelet transform (DWT) to the obtained leaf hyperspectral reflectance to construct the wavelet vegetation index (WVI), analyzed the relationship between the WVI and moso bamboo leaf V25cmax, and compared the WVI to the existing hyperspectral vegetation index (HVI). The ability of the WVI to characterize the moso bamboo V25cmax was interpreted. Finally, the partial least squares regression (PLSR) method was used to construct a model to invert the V25cmax of moso bamboo leaves. We showed the following: (1) There are significant leaf V25cmax differences between I du and II du bamboo, and there are also significant leaf V25cmax differences between the top, middle and bottom canopy positions of I du bamboo. (2) Compared to that with the HVI, the detection wavelength of the V25cmax of the WVI expanded to the shortwave infrared region, and thus the WVI had a higher correlation with the V25cmax. The absolute value of the correlation coefficient between the V25cmax of I du bamboo and SR2148,2188 constructed by cD1 was 0.75, and the absolute value of the correlation coefficient between the V25cmax of II du bamboo and DVI2069,407 constructed by cD2 was 0.67. The highest absolute value of the correlation coefficient between V25cmax and WVI at the three different canopy positions was also 13–21% higher than that with the HVI. The longest wavelength used by the WVI was 2441 nm. (3) The validation accuracies of the V25cmax inversion models constructed with the WVI as a variable were all higher than those of the models constructed with the HVI as a variable for all ages and positions, with the highest R2 value of 0.97 and a reduction of 20–60% in the root mean square error (RMSE) value. After the wavelet decomposition of the hyperspectral reflectance of moso bamboo leaves, the low-frequency components contained no noise, and the high-frequency components highlighted the original spectral detail features. The WVI constructed by these components increases the wavelength range of V25cmax interpretation. Therefore, the V25cmax retrieval model based on the WVI encompasses different resolutions and levels of spectral characteristics, which can better reflect the changes in bamboo leaves and can provide a new method for the inversion of the V25cmax of moso bamboo forests based on hyperspectral remote sensing

Simulating Future LUCC by Coupling Climate Change and Human Effects Based on Multi-Phase Remote Sensing Data

Future land use and cover change (LUCC) simulations play an important role in providing fundamental data to reveal the carbon cycle response of forest ecosystems to LUCC. Subtropical forests have great potential for carbon sequestration, yet their future dynamics under natural and human influences are unclear. Zhejiang Province in China is an important distribution area for subtropical forests. For forest management, it is of great significance to explore the future dynamic changes of subtropical forests in Zhejiang. As a popular LUCC spatial simulation model, the cellular automata (CA) model coupled with machine learning and LUCC quantitative demand models such as system dynamics (SD) can achieve effective LUCC simulation. Therefore, we first integrated a back propagation neural network (BPNN), a CA, and a SD model as a BPNN_CA_SD (BCS) coupled model for future LUCC simulation and then designed a slow development scenario (SD_Scenario), a harmonious development scenario (HD_Scenario), a baseline development scenario (BD_Scenario), and a fast development scenario (FD_Scenario), combining climate change and human disturbance. Thirdly, we obtained future land-use patterns in Zhejiang Province from 2014 to 2084 under multiple scenarios, and finally, we analyzed the temporal and spatial changes of land use and discussed the subtropical forest dynamics of the future. The results showed the following: (1) The overall accuracy was approximately 0.8, the kappa coefficient was 0.75, and the figure of merit (FOM) value was over 28% when using the BCS model to predict LUCC, indicating that the model could predict the consistent change of LUCC accurately. (2) The future evolution of the LUCC under different scenarios varied, with the growth of bamboo forests and the decline of coniferous forests in the FD_Scenario being prominent among the forest dynamics changes. Compared with 2014, the bamboo forest in 2084 will increase by 37%, while the coniferous forest will decrease by 25%. (3) Comparing the area and spatial change of the subtropical forests, the SD_Scenario was found to be beneficial for the forest ecology. These results can provide an important decision-making reference for land-use planning and sustainable forest development in Zhejiang Province

Simulating Future LUCC by Coupling Climate Change and Human Effects Based on Multi-Phase Remote Sensing Data

Future land use and cover change (LUCC) simulations play an important role in providing fundamental data to reveal the carbon cycle response of forest ecosystems to LUCC. Subtropical forests have great potential for carbon sequestration, yet their future dynamics under natural and human influences are unclear. Zhejiang Province in China is an important distribution area for subtropical forests. For forest management, it is of great significance to explore the future dynamic changes of subtropical forests in Zhejiang. As a popular LUCC spatial simulation model, the cellular automata (CA) model coupled with machine learning and LUCC quantitative demand models such as system dynamics (SD) can achieve effective LUCC simulation. Therefore, we first integrated a back propagation neural network (BPNN), a CA, and a SD model as a BPNN_CA_SD (BCS) coupled model for future LUCC simulation and then designed a slow development scenario (SD_Scenario), a harmonious development scenario (HD_Scenario), a baseline development scenario (BD_Scenario), and a fast development scenario (FD_Scenario), combining climate change and human disturbance. Thirdly, we obtained future land-use patterns in Zhejiang Province from 2014 to 2084 under multiple scenarios, and finally, we analyzed the temporal and spatial changes of land use and discussed the subtropical forest dynamics of the future. The results showed the following: (1) The overall accuracy was approximately 0.8, the kappa coefficient was 0.75, and the figure of merit (FOM) value was over 28% when using the BCS model to predict LUCC, indicating that the model could predict the consistent change of LUCC accurately. (2) The future evolution of the LUCC under different scenarios varied, with the growth of bamboo forests and the decline of coniferous forests in the FD_Scenario being prominent among the forest dynamics changes. Compared with 2014, the bamboo forest in 2084 will increase by 37%, while the coniferous forest will decrease by 25%. (3) Comparing the area and spatial change of the subtropical forests, the SD_Scenario was found to be beneficial for the forest ecology. These results can provide an important decision-making reference for land-use planning and sustainable forest development in Zhejiang Province

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 &gt;76.26% and an accuracy of users of &gt;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&middot;ha&minus;1, 10.95 Mg&middot;ha&minus;1, 15.25 Mg&middot;ha&minus;1 and 19.07 Mg&middot;ha&minus;1 respectively, and the average annual growth of 0.88 Mg&middot;ha&minus;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

Remote Sensing Estimation of Bamboo Forest Aboveground Biomass Based on Geographically Weighted Regression

Bamboo forests are widespread in subtropical areas and are well known for their rapid growth and great carbon sequestration ability. To recognize the potential roles and functions of bamboo forests in regional ecosystems, forest aboveground biomass (AGB)—which is closely related to forest productivity, the forest carbon cycle, and, in particular, carbon sinks in forest ecosystems—is calculated and applied as an indicator. Among the existing studies considering AGB estimation, linear or nonlinear regression models are the most frequently used; however, these methods do not take the influence of spatial heterogeneity into consideration. A geographically weighted regression (GWR) model, as a spatial local model, can solve this problem to a certain extent. Based on Landsat 8 OLI images, we use the Random Forest (RF) method to screen six variables, including TM457, TM543, B7, NDWI, NDVI, and W7B6VAR. Then, we build the GWR model to estimate the bamboo forest AGB, and the results are compared with those of the cokriging (COK) and orthogonal least squares (OLS) models. The results show the following: (1) The GWR model had high precision and strong prediction ability. The prediction accuracy (R2) of the GWR model was 0.74, 9%, and 16% higher than the COK and OLS models, respectively, while the error (RMSE) was 7% and 12% lower than the errors of the COK and OLS models, respectively. (2) The bamboo forest AGB estimated by the GWR model in Zhejiang Province had a relatively dense spatial distribution in the northwestern, southwestern, and northeastern areas. This is in line with the actual bamboo forest AGB distribution in Zhejiang Province, indicating the potential practical value of our study. (3) The optimal bandwidth of the GWR model was 156 m. By calculating the variable parameters at different positions in the bandwidth, close attention is given to the local variation law in the estimation of the results in order to reduce the model error

Spatiotemporal Evolution of Fractional Vegetation Cover and Its Response to Climate Change Based on MODIS Data in the Subtropical Region of China

The subtropical vegetation plays an important role in maintaining the structure and function of global ecosystems, and its contribution to the global carbon balance are receiving increasing attention. The fractional vegetation cover (FVC) as an important indicator for monitoring environment change, is widely used to analyze the spatiotemporal pattern of regional and even global vegetation. China is an important distribution area of subtropical vegetation. Therefore, we first used the dimidiate pixel model to extract the subtropical FVC of China during 2001–2018 based on MODIS land surface reflectance data, and then used the linear regression analysis and the variation coefficient to explore its spatiotemporal variations characteristics. Finally, the partial correlation analysis and the partial derivative model were used to analyze the influences and contributions of climate factors on FVC, respectively. The results showed that (1) the subtropical FVC had obvious spatiotemporal heterogeneity; the FVC high-coverage and medium-coverage zones were concentratedly and their combined area accounted for more than 70% of the total study area. (2) The interannual variation in the average subtropical FVC from 2001 to 2018 showed a significant growth trend. (3) In 76.28% of the study area, the regional FVC showed an increasing trend, and the remaining regional FVC showed a decreasing trend. However, the overall fluctuations in the FVC (increasing or decreasing) in the region were relatively stable. (4) The influences of climate factors to the FVC exhibited obvious spatial differences. More than half of all pixels exhibited the influence of the average annual minimum temperature and the annual precipitation had positive on FVC, while the average annual maximum temperature had negative on FVC. (5) The contributions of climate changes to FVC had obvious heterogeneity, and the average annual minimum temperature was the main contribution factor affecting the dynamic variations of FVC

Spatiotemporal LUCC Simulation under Different RCP Scenarios Based on the BPNN_CA_Markov Model: A Case Study of Bamboo Forest in Anji County

Simulating spatiotemporal land use and land cover change (LUCC) data precisely under future climate scenarios is an important basis for revealing the carbon cycle response of forest ecosystems to LUCC. In this paper, a coupling model consisting of a back propagation neural network (BPNN), Markov chain, and cellular automata (CA) was designed to simulate the LUCC in Anji County, Zhejiang Province, under four climate scenarios (Representative Concentration Pathway (RCP) 2.6, 4.5, 6.0, 8.5) from 2024 to 2049 and to analyze the temporal and spatial distribution of bamboo forests in Anji County. Our results provide four outcomes. (1) The transition probability matrices indicate that the area of bamboo forests shows an expansion trend, and the largest contribution to the expansion of bamboo forests is the cultivated land. The Markov chain composed of the average transition probability matrix could perform excellently, with only small errors when simulating the areas of different land-use types. (2) Based on the optimized BPNN, which had a strong generalization ability, a high prediction accuracy, and area under the curve (AUC) values above 0.9, we could obtain highly reliable land suitability probabilities. After introducing more driving factors related to bamboo forests, the prediction of bamboo forest changes will be more accurate. (3) The BPNN_CA_Markov coupling model could achieve high-precision simulation of LUCC at different times, with an overall accuracy greater than 70%, and the consistency of the LUCC simulation from one time to another also had good performance, with a figure of merit (FOM) of approximately 40%. (4) Under the future four RCP scenarios, bamboo forest evolution had similar spatial characteristics; that is, bamboo forests were projected to expand in the northeast, south, and southwest mountainous areas of Anji County, while bamboo forests were projected to decline mainly around the junction of the central and mountainous areas of Anji County. Comparing the simulation results of different scenarios demonstrates that 74% of the spatiotemporal evolution of bamboo forests will be influenced by the interactions and competition among different land-use types and other driving factors, and 26% will come from different climate scenarios, among which the RCP8.5 scenario will have the greatest impact on the bamboo forest area and spatiotemporal evolution, while the RCP2.6 scenario will have the smallest impact. In short, this study proposes effective methods and ideas for LUCC simulation in the context of climate change and provides accurate data support for analyzing the impact of LUCC on the carbon cycle of bamboo forests

Spatiotemporal Evolution of Fractional Vegetation Cover and Its Response to Climate Change Based on MODIS Data in the Subtropical Region of China

The subtropical vegetation plays an important role in maintaining the structure and function of global ecosystems, and its contribution to the global carbon balance are receiving increasing attention. The fractional vegetation cover (FVC) as an important indicator for monitoring environment change, is widely used to analyze the spatiotemporal pattern of regional and even global vegetation. China is an important distribution area of subtropical vegetation. Therefore, we first used the dimidiate pixel model to extract the subtropical FVC of China during 2001–2018 based on MODIS land surface reflectance data, and then used the linear regression analysis and the variation coefficient to explore its spatiotemporal variations characteristics. Finally, the partial correlation analysis and the partial derivative model were used to analyze the influences and contributions of climate factors on FVC, respectively. The results showed that (1) the subtropical FVC had obvious spatiotemporal heterogeneity; the FVC high-coverage and medium-coverage zones were concentratedly and their combined area accounted for more than 70% of the total study area. (2) The interannual variation in the average subtropical FVC from 2001 to 2018 showed a significant growth trend. (3) In 76.28% of the study area, the regional FVC showed an increasing trend, and the remaining regional FVC showed a decreasing trend. However, the overall fluctuations in the FVC (increasing or decreasing) in the region were relatively stable. (4) The influences of climate factors to the FVC exhibited obvious spatial differences. More than half of all pixels exhibited the influence of the average annual minimum temperature and the annual precipitation had positive on FVC, while the average annual maximum temperature had negative on FVC. (5) The contributions of climate changes to FVC had obvious heterogeneity, and the average annual minimum temperature was the main contribution factor affecting the dynamic variations of FVC