Assimilating spatiotemporal MODIS LAI data with a particle filter algorithm for improving carbon cycle simulations for bamboo forest ecosystems.

Bamboo forests are an important part of the forest ecosystem, which has strong carbon sequestration potential and plays an important role in the global carbon cycle. As a key parameter for simulating the carbon cycle using forest ecosystem models, the quality of leaf area index (LAI) data has a direct influence on the accuracy of modelling results. Here, we used the particle filter (PF) algorithm and PROSAIL model to assimilate MODIS LAI products, which were then used to drive a boreal ecosystem productivity simulator model to simulate the bamboo forest carbon cycle. The results showed that the relationship between the assimilated and observed LAI values was very significant, with an R2 of 0.95 and an RMSE of 0.28, greatly improving the precision of MODIS LAI products. The R2 values for the gross primary productivity (GPP), net ecosystem exchange (NEE), and total ecosystem respiration (TER) simulated by the assimilated LAI values and observed carbon fluxes were 0.65, 0.45 and 0.70, respectively, and the RMSE values were 1.10 g C m-2 day-1, 1.00 g C m-2 day-1 and 0.35 g C m-2 day-1, respectively. Compared with the results of the carbon cycle simulated by non-assimilated LAI, the R2 values of the GPP, NEE and TER values that were simulated by assimilated LAI increased by 27.5%, 45.2% and 6.1%, and the RMSE values decreased by 29.9%, 23.7% and 22.2%, respectively. Therefore, coupling the PF and PROSAIL models can greatly improve the simulation precision for the large-scale bamboo forest carbon cycle. This study laid the foundation for simulating the carbon cycle over a large-scale bamboo forest based on low-resolution data in the future.

Spatiotemporal evolution and impacts of climate change on bamboo distribution in China.

Understanding the impact and restriction of climate change on potential distribution of bamboo forest is crucial for sustainable management of bamboo forest and bamboo-based economic development. In this study, climatic variables and maximum entropy model were used to simulate the potential distribution of bamboo forest in China under the future climate scenarios. Seven climatic variables, such as Spring precipitation, Summer precipitation, Autumn precipitation, average annual relative humidity, Autumn average temperature, average annual temperature range and annual total radiation, were selected as input variables of maximum entropy model based on the relative importance of those climate variables for predicting bamboo forest presence. The suitable ranges of the seven climatic variables for potential distribution of bamboo forest were 337-794 mm, 496-705 mm, 213-929 mm, 74.3%-83.4%, 16.6-23.8 °C, 2.3-10.1 °C and 3.2 × 104-4.3 × 104 W m-2, respectively. Under RCP4.5 and RCP8.5 climate scenarios, the suitable area of bamboo forest growth first increased and then decreased, and showed range contractions towards the interior and expansions towards southwest in China. The results of the present study can serve as a useful reference to dynamic monitoring of the spatial distribution and sustainable utilization of bamboo forest in the future under climate change.

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 .

[Retrieval of leaf area index of Phyllostachys praecox forest based on MODIS reflectance time series data.] / 基于MODIS时间序列反射率数据的雷竹林LAI反演.

Based on the MODIS surface reflectance data, five vegetation indices, including norma-lized difference vegetation index (NDVI), simple ratio index (SR), Gitelson green index (GI), enhanced vegetation index (EVI) and soil adjusted vegetation index (SAVI) were constructed as remote sensing variables, coupled with the seven original spectral reflectance bands of MODIS. Stepwise regression and correlation analysis were used to select the variables, and the stepwise regression and Back Propagation (BP) neural network models were constructed based on the measured LAI to retrieve the LAI time series data of Phyllostachys praecox (Lei bamboo) forest during the period from January 2014 to March 2017. The retrieval results were compared with MOD15A2 LAI products during the same period. The results showed that SR was the single variable selected for the stepwise regression model. The correlations of LAI with bands b1, b2, b3, b7 and five vegetation indices were significant, which could be used as input variables of BP neural network model. There was a significant correlation between the LAI estimated from BP neural network and measured LAI, with the R2 of 0.71, RMSE of 0.34, and RMSEr of 13.6%. R2 was increased by 10.9%, RMSE decreased by 5.6%, and RMSEr decreased by 12.3% compared with LAI estimated from stepwise regression method. R2 was increased by 54.5%, RMSE decreased by 79.3%, and RMSEr decreased by 79.1% compared with MODIS LAI. The LAI of Lei bamboo forest could be accurately retrieved using BP neural network method based on MODIS reflectance time series data, which would be a feasible method for rapid monitoring of LAI in Lei bamboo forest.

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 ±â€¯23.1, 1264.5 ±â€¯45.2, and 1369.6 ±â€¯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.