Coupled LAI assimilation and BEPS model for analyzing the spatiotemporal pattern and heterogeneity of carbon fluxes of the bamboo forest in Zhejiang Province, China

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.

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Eddy covariance analysis of the implications of drought on the carbon fluxes of Moso bamboo forest in southeastern China

Trees ◽

10.1007/s00468-016-1414-5 ◽

2016 ◽

Vol 30 (5) ◽

pp. 1807-1820 ◽

Cited By ~ 7

Author(s):

Xiaojun Xu ◽

Huaqiang Du ◽

Guomo Zhou ◽

Pingheng Li ◽

Yongjun Shi ◽

...

Keyword(s):

Eddy Covariance ◽

Carbon Fluxes ◽

Covariance Analysis ◽

Moso Bamboo ◽

Bamboo Forest ◽

Southeastern China ◽

Moso Bamboo Forest

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Estimating and Analyzing the Spatiotemporal Pattern of Aboveground Carbon in Bamboo Forest by Combining Remote Sensing Data and Improved BIOME-BGC Model

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2018.2817344 ◽

2018 ◽

Vol 11 (7) ◽

pp. 2282-2295 ◽

Cited By ~ 4

Author(s):

Huaqiang Du ◽

Yangguang Li ◽

Fangjie Mao ◽

Guomo Zhou ◽

Xuejian Li ◽

...

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Spatiotemporal Pattern ◽

Bamboo Forest ◽

Sensing Data ◽

Aboveground Carbon

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Diurnal and seasonal variations in carbon ﬂuxes in bamboo forests during the growing season in Zhejiang province, China

10.5194/egusphere-egu2020-21912 ◽

2020 ◽

Author(s):

Liang Chen

Keyword(s):

Forest Type ◽

Ecosystem Respiration ◽

Net Ecosystem Exchange ◽

Growing Season ◽

Zhejiang Province ◽

Carbon Dioxide Exchange ◽

Bamboo Forest ◽

Growing Period ◽

Control Factors ◽

Bamboo Shoots

<p>Bamboo forest is an important forest type in subtropical China and is characterized by fast growth and high carbon sequestration capacity. However, the dynamics of carbon &#64258;uxes during the fast growing period of bamboo shoots and their correlation with environment factors are poorly understood. We measured carbon dioxide exchange and climate variables using open-path eddy covariance methods during the 2011 growing season in a Moso bam-boo forest (MB, Phyllostchys edulis) and a Lei bamboo. forest (LB, Phyllostachys violascens) in Zhejiang province, China. The bamboo forests were carbon sinks during the growing season. The minimum diurnal net ecosystem exchange (NEE) at MB and LB sites were - 0.64 and - 0.66 mg C m-2 s-1, respectively. The minimum monthly NEE, ecosystem respiration (RE), and gross ecosystem exchange (GEE) were - 99.3 &#177; 4.03, 76.2 &#177; 2.46, and - 191.5 &#177; 4.98 g C m-2 month-1, respectively, at MB site, compared with - 31.8 &#177; 3.44, 70.4 &#177; 1.41, and - 157.9 &#177; 4.86 g C m-2 month-1, respectively, at LB site. Maximum RE was 92.1 &#177; 1.32 g C m-2 month-1 at MB site and 151.0 &#177; 2.38 g C m-2 month-1 at LB site. Key control factors varied by month during the growing season, but across the whole growing season, NEE and GEE at both sites showed similar trends in sensitivities to photosynthetic active radiation and vapor pressure de&#64257;cit, and air temperature had the strongest correlation with RE at both sites. Carbon &#64258;uxes at LB site were more sensitive to soil water content compared to those at MB site. Both on-year (years when many new shoots are produced) and off-year (years when none or few new shoots are produced) should be studied in bamboo forests to better understand their role in global carbon cycling.</p>

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Biotic and abiotic influences on monthly variation in carbon fluxes in on-year and off-year Moso bamboo forest

Trees ◽

10.1007/s00468-018-1765-1 ◽

2018 ◽

Vol 33 (1) ◽

pp. 153-169 ◽

Cited By ~ 5

Author(s):

Yufeng Zhou ◽

Guomo Zhou ◽

Huaqiang Du ◽

Yongjun Shi ◽

Fangjie Mao ◽

...

Keyword(s):

Carbon Fluxes ◽

Moso Bamboo ◽

Bamboo Forest ◽

Monthly Variation ◽

Moso Bamboo Forest ◽

Abiotic Influences

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Spatiotemporal Evolution of the Carbon Fluxes from Bamboo Forests and their Response to Climate Change Based on a BEPS Model in China

Remote Sensing ◽

10.3390/rs14020366 ◽

2022 ◽

Vol 14 (2) ◽

pp. 366

Author(s):

Fangfang Kang ◽

Xuejian Li ◽

Huaqiang Du ◽

Fangjie Mao ◽

Guomo Zhou ◽

...

Keyword(s):

Primary Productivity ◽

Net Primary Productivity ◽

Climatic Factors ◽

Carbon Fluxes ◽

Negative Effects ◽

Spatiotemporal Evolution ◽

Positive Effects ◽

The North ◽

Boreal Ecosystem ◽

Beps Model

Carbon flux is the main basis for judging the carbon source/sink of forest ecosystems. Bamboo forests have gained much attention because of their high carbon sequestration capacity. In this study, we used a boreal ecosystem productivity simulator (BEPS) model to simulate the gross primary productivity (GPP) and net primary productivity (NPP) of bamboo forests in China during 2001–2018, and then explored the spatiotemporal evolution of the carbon fluxes and their response to climatic factors. The results showed that: (1) The simulated and observed GPP values exhibited a good correlation with the determination coefficient (R2), root mean square error (RMSE), and absolute bias (aBIAS) of 0.58, 1.43 g C m−2 day−1, and 1.21 g C m−2 day−1, respectively. (2) During 2001–2018, GPP and NPP showed fluctuating increasing trends with growth rates of 5.20 g C m−2 yr−1 and 3.88 g C m−2 yr−1, respectively. The spatial distribution characteristics of GPP and NPP were stronger in the south and east than in the north and west. Additionally, the trend slope results showed that GPP and NPP mainly increased, and approximately 30% of the area showed a significant increasing trend. (3) Our study showed that more than half of the area exhibited the fact that the influence of the average annual precipitation had positive effects on GPP and NPP, while the average annual minimum and maximum temperatures had negative effects on GPP and NPP. On a monthly scale, our study also demonstrated that the influence of precipitation on GPP and NPP was higher than that of the influence of temperature on them.

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