Research on Ecoenvironmental Quality Evaluation System Based on Big Data Analysis.

Comprehensive and objective evaluation of ecological environment quality is of great significance to regional sustainable development. In this study, Landsat remote sensing images of 1991, 2000, 2004, 2010, 2013, 2018, and 2019 are selected to evaluate the changes of ecological environment quality in the Headwaters of Dongjiangyuan River by using remote sensing ecological index RSEI. The influencing factors of ecological environment change in Dongjiangyuan River are also discussed. The results showed that, from 1991 to 2019, the ecoenvironmental quality of the Dongjiangyuan River showed a good trend of development. Humidity index, greenness index, and dryness index all fluctuated in a small range; the greenness and dryness showed an overall increase. The average temperature in the Headwaters of the Dongjiangyuan River presents a rising trend. This study establishes the evaluation system of ecological environment quality from two dimensions of time and space and gives the change rule of environmental quality quantitatively, which provides the theoretical basis for the ecological environment management of Dongjiangyuan River.

Spring Leafing Phenology Favors Younger Culms of Moso Bamboo: Aspects From Water Use Relations.

As the most widely distributed giant running bamboo species in China, Moso bamboo (Phyllostachys edulis) can accomplish both development of newly sprouted culms and leaf renewal of odd-year-old culms within a few months in spring. The two phenological events in spring may together change water distribution among culms in different age categories within a stand, which may differ from our conventional understanding of the negative age effect on bamboo water use. Therefore, to explore the effect of spring shooting and leaf phenology on age-specific water use of Moso bamboo and potential water redistribution, we monitored water use of four culm age categories (newly sprouted, 1-, 2-, and 3-year-old; namely A0, A1, A2, A3) in spring from March to June 2018. For newly sprouting culms, the spring phenological period was classified into five stages (incubation, culm-elongation, branch-development, leafing, established). Over these phenological stages, age-specific accumulated sap flux density showed different patterns. The oldest culms, A3, were not influenced by leaf renewal and kept nearly constant and less water use than the other aged culms. However, A2, which did not renew their leaves, had the most water use at the two initial stages (incubation, culm-elongation) but consumed less water than A0 and A1 after the fourth stage (leafing). At the end of June, water use of the four age categories sorted in order of A0 > A1 > A2 > A3, which confirms the conventional thought and observations, i.e., a negative age effect. The results indicate that new leaf flushing may benefit younger culms (A1 and A0) more than older culms (A2 and A3), i.e., increasing their transpiration response to radiation and share of the stand transpiration. With the underground connected rhizome system, the bamboo stand as an integration seems to balance its water use among culms of different ages to support the water use of freshly sprouted culms during their developing period.

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.

[Retrieval of leaf net photosynthetic rate of moso bamboo forests using hyperspectral remote sen-sing based on wavelet transform].

This study focused on retrieval of net photosynthetic rate (Pn) of moso bamboo forest based on analysis of wavelet transform on hyperspectral reflectance data of moso bamboo forest leaf. The result showed that the accuracy of Pn retrieved by the ideal high frequency wavelet vegetation index ( VI) was higher than that retrieved by low frequency wavelet VI and spectral VI. Normalized difference vegetation index of wavelet (NDVIw), simple ratio vegetation index of wavelet (SRw) and difference vegetation index of wavelet (Dw) constructed by the first layer of high frequency coefficient through wavelet decomposition had the highest relationship with Pn, with the R² of 0.7 and RMSE of 0.33; low frequency wavelet VI had no advantage compared with spectral VI. Significant correlation existed between Pn estimated by multivariate linear model constructed by the ideal wavelet VI and the measured Pn, with the R² of 0.77 and RMSE of 0.29, and the accuracy was significantly higher than that of using the spectral VI. Compared with the fact that sensitive spectral bands of the retrieval through spectral VI were limited in the range of visible light, the wavelength of sensitive bands of wavelet VI ranged more widely from visible to infrared bands. The results illustrated that spectrum of wavelet transform could reflect the Pn of moso bamboo more in detail, and the overall accuracy was significantly improved than that using the original spectral data, which provided a new alternative method for retrieval of Pn of moso bamboo forest using hyper spectral remotely sensed data.

Development of the BIOME-BGC model for the simulation of managed Moso bamboo forest ecosystems.

Numerical models are the most appropriate instrument for the analysis of the carbon balance of terrestrial ecosystems and their interactions with changing environmental conditions. The process-based model BIOME-BGC is widely used in simulation of carbon balance within vegetation, litter and soil of unmanaged ecosystems. For Moso bamboo forests, however, simulations with BIOME-BGC are inaccurate in terms of the growing season and the carbon allocation, due to the oversimplified representation of phenology. Our aim was to improve the applicability of BIOME-BGC for managed Moso bamboo forest ecosystem by implementing several new modules, including phenology, carbon allocation, and management. Instead of the simple phenology and carbon allocation representations in the original version, a periodic Moso bamboo phenology and carbon allocation module was implemented, which can handle the processes of Moso bamboo shooting and high growth during "on-year" and "off-year". Four management modules (digging bamboo shoots, selective cutting, obtruncation, fertilization) were integrated in order to quantify the functioning of managed ecosystems. The improved model was calibrated and validated using eddy covariance measurement data collected at a managed Moso bamboo forest site (Anji) during 2011-2013 years. As a result of these developments and calibrations, the performance of the model was substantially improved. Regarding the measured and modeled fluxes (gross primary production, total ecosystem respiration, net ecosystem exchange), relative errors were decreased by 42.23%, 103.02% and 18.67%, respectively.

Improving Estimations of Spatial Distribution of Soil Respiration Using the Bayesian Maximum Entropy Algorithm and Soil Temperature as Auxiliary Data.

Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to study the spatial distribution of soil respiration. The BME algorithm used the soft data (auxiliary information) effectively to improve the estimation accuracy of the spatiotemporal distribution of soil respiration. Based on the functional relationship between soil temperature and soil respiration, the BME algorithm satisfactorily integrated soil temperature data into said spatial distribution. As a means of comparison, we also applied the Ordinary Kriging (OK) and Co-Kriging (Co-OK) methods. The results indicated that the root mean squared errors (RMSEs) and absolute values of bias for both Day 1 and Day 2 were the lowest for the BME method, thus demonstrating its higher estimation accuracy. Further, we compared the performance of the BME algorithm coupled with auxiliary information, namely soil temperature data, and the OK method without auxiliary information in the same study area for 9, 21, and 37 sampled points. The results showed that the RMSEs for the BME algorithm (0.972 and 1.193) were less than those for the OK method (1.146 and 1.539) when the number of sampled points was 9 and 37, respectively. This indicates that the former method using auxiliary information could reduce the required number of sampling points for studying spatial distribution of soil respiration. Thus, the BME algorithm, coupled with soil temperature data, can not only improve the accuracy of soil respiration spatial interpolation but can also reduce the number of sampling points.

Current and potential carbon stocks in Moso bamboo forests in China.

Bamboo forests provide important ecosystem services and play an important role in terrestrial carbon cycling. Of the approximately 500 bamboo species in China, Moso bamboo (Phyllostachys pubescens) is the most important one in terms of distribution, timber value, and other economic values. In this study, we estimated current and potential carbon stocks in China's Moso bamboo forests and in their products. The results showed that Moso bamboo forests in China stored about 611.15 ± 142.31 Tg C, 75% of which was in the top 60 cm soil, 22% in the biomass of Moso bamboos, and 3% in the ground layer (i.e., bamboo litter, shrub, and herb layers). Moso bamboo products store 10.19 ± 2.54 Tg C per year. The potential carbon stocks reach 1331.4 ± 325.1 Tg C, while the potential C stored in products is 29.22 ± 7.31 Tg C a(-1). Our results indicate that Moso bamboo forests and products play a critical role in C sequestration. The information gained in this study will facilitate policy decisions concerning carbon sequestration and management of Moso bamboo forests in China.