Fangjie Mao

Optimizing selective cutting strategies for maximum carbon stocks and yield of Moso bamboo forest using BIOME-BGC model

The selective cutting method currently used in Moso bamboo forests has resulted in a reduction of stand productivity and carbon sequestration capacity. Given the time and labor expense involved in addressing this problem manually, simulation using an ecosystem model is the most suitable approach. The BIOME-BGC model was improved to suit managed Moso bamboo forests, which was adapted to include age structure, specific ecological processes and management measures of Moso bamboo forest. A field selective cutting experiment was done in nine plots with three cutting intensities (high-intensity, moderate-intensity and low-intensity) during 2010-2013, and biomass of these plots was measured for model validation. Then four selective cutting scenarios were simulated by the improved BIOME-BGC model to optimize the selective cutting timings, intervals, retained ages and intensities. The improved model matched the observed aboveground carbon density and yield of different plots, with a range of relative error from 9.83% to 15.74%. The results of different selective cutting scenarios suggested that the optimal selective cutting measure should be cutting 30% culms of age 6, 80% culms of age 7, and all culms thereafter (above age 8) in winter every other year. The vegetation carbon density and harvested carbon density of this selective cutting method can increase by 74.63% and 21.5%, respectively, compared with the current selective cutting measure. The optimized selective cutting measure developed in this study can significantly promote carbon density, yield, and carbon sink capacity in Moso bamboo forests.

Comparison of Two Data Assimilation Methods for Improving MODIS LAI Time Series for Bamboo Forests

Bamboo forests, especially the Moso bamboo forest (MBF) and the Lei bamboo forest (LBF), have a strong carbon sequestration capability and play an important role in the global forest carbon cycle. The leaf area index (LAI) is an important structural parameter for simulating the spatiotemporal pattern of the carbon cycle in bamboo forests. However, current LAI products suffer from substantial noise and errors, and data assimilation methods are the most appropriate way to improve the accuracy of LAI data. In this study, two data assimilation methods (the Dual Ensemble Kalman filter (DEnKF) and Particle filter (PF) methods) were applied to improve the quality of MODIS LAI time-series data, which removed noises and smoothed the results using a locally adjusted cubic-spline capping method for the MBF and LBF during 2014–2015. The method with the highest correlation coefficient (r) and lowest root-mean-square error (RMSE) was used to generate highly accurate LAI products of bamboo forests in Zhejiang Province. The results show that the LAI assimilated using two methods saw greatly reduced fluctuations in the MODIS LAI product for both the MBF and the LBF. The LAI assimilated using DEnKF significantly correlated with the observed LAI, with an r value of 0.90 and 0.95, and an RMSE value of 0.42 and 0.42, for the MBF and the LBF, respectively. The PF algorithm achieved a better accuracy than the DEnKF algorithm, with an average increase in r of 8.78% and an average decrease in the RMSE of 33.33%. Therefore, the PF method was applied for LAI assimilation in Zhejiang Province, and the assimilated LAI of bamboo forests achieved a reasonable spatiotemporal pattern in Zhejiang Province. The PF algorithm greatly improves the accuracy of MODIS LAI products and provides a reliable structural parameter for the large-scale simulation of the carbon cycle in bamboo forest ecosystems.

Global sensitivity analysis of PROSAIL model parameters when simulating Moso bamboo forest canopy reflectance

ABSTRACT PROSAIL is a combination of the leaf optical properties spectra (PROSPECT) model and the scattering by arbitrarily inclined leaves (SAIL) canopy bidirectional reflectance model. When modelling forest canopy reflectance using the PROSAIL radiative transfer model, the sensitivities of parameters can affect the modelling accuracy. Traditionally, sensitivities have been assessed using local sensitivity analysis (LSA); however, drawbacks to this approach include a lack of consideration for coupled effects between different parameters. In this study, parameter sensitivities in the PROSAIL model were calculated using two global sensitivity analysis (GSA) methods (the Extended Fourier Amplitude Sensitivity Test (EFAST) method and the Morris method), field measurements, and Landsat 5 Thematic Mapper (TM) data for a Moso bamboo forest. The results of GSA were compared with those of LSA in order to identify the key parameters impacting the Moso bamboo forest canopy reflectance, and to provide a reference for model optimization and vegetation canopy inversion improvement. The results showed that: (1) the sensitivities of six major input parameters of the PROSAIL model were generally consistent with the sorting orders of the two GSA methods, but were not in accordance with those from the LSA method, especially in the mid-infrared band; (2) coupled effects among parameters acting on reflectance simulation in visible light bands were greater than those in infrared bands; (3) the simulated canopy reflectance was evaluated using Landsat 5 TM data, and the results simulated based on LSA analysis showed higher error than those based on GSA analysis, because the LSA method ignored the influence of some parameters on canopy reflectance, e.g. leaf mesophyll structure (N), average leaf angle (ALA), leaf water content (Cw), and leaf dry matter content (Cm). However, GSA was able to fully consider the coupled effects among parameters, and thus identified the sensitive parameters impacting on reflectance more accurately.

Estimation of gross primary production in Moso bamboo forest based on light-use efficiency derived from MODIS reflectance data

ABSTRACT Assessing the contribution of Moso bamboo (Phyllostachys pubescens) forest to forest ecosystem carbon storage requires accurate estimation of gross primary production (GPP). Based on measurements of light-use efficiency (LUE), defined as the ratio of measured GPP to photosynthetically active radiation (PAR), from the eddy covariance flux tower, the linear regression model and partial least squares regression model were used for estimation of LUE using the Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance data. GPP estimates were then calculated by the product of LUE estimates and PAR (named the LUE-PAR model), which was compared with GPP from the GPP algorithm designed for the MODIS sensor aboard the Aqua and Terra platforms (MOD17A2 model) and the EC-LUE model. The results revealed the PLS model performed better than the linear regression model in LUE estimation but had lager uncertainties in high and low LUE values. GPP estimates driven by a MODIS-based radiation product with high spatial resolution was more accurate than those driven by Modern-Era Retrospective Analysis for Research and Applications (MERRA) radiation product from the NASA’s Global Modelling and Assimilation Office data set. The LUE-PAR model had the highest accuracy than the other two LUE models. The GPP values derived from the EC-LUE model driven by photosynthetically active radiation (PAR) from MERRA and maximum LUE from the EC data were overestimated due to the overestimation in MERRA radiation product. The GPP values derived from the MOD17A2 model driven by PAR from the MERRA and maximum LUE from the biome properties look-up table were underestimated due to underestimation in the maximum LUE of Moso bamboo forest. This study implied that the LUE-PAR model driven by LUE estimates from the PLS model and PAR from MERRA is a superior approach in improving GPP simulations, and PAR products with high spatial resolution and accurate species-specific maximum LUE are necessary for the LUE models in estimating GPP at regional scale.

Remote estimation of canopy leaf area index and chlorophyll content in Moso bamboo ( Phyllostachys edulis (Carrière) J. Houz.) forest using MODIS reflectance data

Key messageWe estimated the leaf area index (LAI) and canopy chlorophyll content (CC) of Moso bamboo forest by using statistical models based on MODIS data and field measurements. Results showed that the statistical model driven by MODIS data has the potential to accurately estimate LAI and CC, while the structure of the calibration models varied between on- and off-years because of the different leaf change and bamboo shoot production characteristics between these types of years.ContextLAI and CC (gram per square meter of ground area) are important parameters for determining carbon exchange between Moso bamboo forest (Phyllostachys edulis (Carrière) J. Houz.) and the atmosphere.AimsThis study evaluated the ability of a statistical model driven by MODIS data to accurately estimate the LAI and CC in Moso bamboo forest, and differences in the LAI and CC between on-years (years with great shoot production) and off-years (years with less shoot production) were analyzed.MethodsThe LAI and CC measurements were collected in Anji County, Zhejiang Province, China. Indicators of LAI and CC were calculated from MODIS data. Then, a regression analysis was used to build relationships between the LAI and CC and various indicators on the basis of leaf change and bamboo shoot production characteristics of Moso bamboo forest.ResultsLAI and CC were accurately estimated by using the regression analysis driven by MODIS-derived indicators with a relative root mean squared error (RMSEr) of 9.04 and 13.1%, respectively. The structure of the calibration models varied between on- and off-years. Long-term time series analysis from 2000 to 2015 showed that LAI and CC differed largely between on- and off-years.ConclusionThis study demonstrates that LAI and CC of Moso bamboo forest can be estimated accurately by using a statistical model driven by MODIS-derived indicators, but attention should be paid to differences in the calibration models between on- and off-years.

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

Key messageMonthly variation in gross primary productivity (GPP) between on-years and off-years were different. Main drivers of GPP in on-years were abiotic. In off-years drivers were biotic and abiotic.AbstractUnderstanding biotic (living or once-living organisms) and abiotic (non-living physical and chemical elements) influences on seasonal variation in carbon fluxes in Moso bamboo forest is important for predicting future carbon sequestration under climate change. Although differing physiological and ecological characteristics of Moso bamboo forest between on-years and off-years have been observed, the drivers of annual differences in carbon fluxes remain unknown. In this study, drivers of variation in carbon fluxes were analyzed based on gross primary productivity (GPP) and biotic factors (leaf area and chlorophyll content here, represented by vegetation indices—VIs) and abiotic factors. Results showed that average monthly GPP between on-years and off-years was significantly different from January to June, mainly due to natural variation in biotic factors. The monthly variation in GPP during on-years was mainly influenced by abiotic factors, whereas that in off-years was determined by the combination of biotic and abiotic factors. Monthly variation and differences in GPP between on-years and off-years were well represented by VIs. The GPP was more strongly correlated with VIs in off-years than in on-years, owing to large seasonal variation in canopy chlorophyll content. Hence, GPP estimated from both air temperature and simple ratio was more accurate than that estimated from air temperature alone. Overall, the difference in GPP between on-years and off-years and its underlying mechanisms can be used to accurately estimate carbon fluxes in Moso forest and predict carbon fluxes under future climate warming.

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.

Response of carbon uptake to abiotic and biotic drivers in an intensively managed Lei bamboo forest

Lei bamboo (Phyllostachys praecox) is widely distributed in southeastern China. We used eddy covariance to analyze carbon sequestration capacity of a Lei bamboo forest (2011-2013) and to identify the seasonal biotic and abiotic determinants of carbon fluxes. A machine learning algorithm called random forest (RF) was used to identify factors that affected carbon fluxes. The RF model predicted well the gross ecosystem productivity (GEP), ecosystem respiration (RE) and net ecosystem exchange (NEE), and displayed variations in the drivers between different seasons. Mean annual NEE, RE, and GEP were -105.2 ± 23.1, 1264.5 ± 45.2, and 1369.6 ± 52.5 g C m-2, respectively. Climate warming increased RE more than GEP when water inputs were not limiting. Summer drought played little role in suppressing GEP, but low soil moisture contents suppressed RE and increased the carbon sink during drought in the summer. The most important drivers of NEE were soil temperature in spring, summer, and winter, and photosynthetically active radiation in autumn. Air and soil temperature were important drivers of GEP in all seasons.