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Cyanobacterial biomass is important for biofuel and biofertilizer, however, biomass production requires expensive chemical growth nutrients. To address this issue, we explored the useof inexpensive growth nutrient media from an integrated manure-seawater system for cyanobacterial biomass production. Salt-tolerant cyanobacterial strain HSaC and salt-sensitive cyanobacterial strain LC were tested to evaluate the potential of integrated manure-seawater media for sustainable cyanobacterial biomass production. As a prerequisite for seawater experiments, strain HSaC was grown at different NaCl concentrations (0 mM, 60 mM, 120 mM, 180 mM, 240 mM and 300 mM) to identify the optimum salt concentration. The highest biomass yield and photosynthetic pigment contents were obtained at 120 mM NaCl concentration. The highest exo-polysaccharide (EPS) content was obtained at 180 mM NaCl concentration. The treatments for the manure-seawater media were cow manure, pig manure, chicken manure and BG11, each with distilled water, diluted seawater and non-diluted seawater. The highest biomass and photosynthetic pigment yield for cyanobacterial strains LC and HSaC were obtained from 0.5 dS/m and 10 dS/m diluted seawater integrated with cow manure, respectively, but pig and chicken manure performed poorly. Overall, the biomass production and photosynthetic pigment results from cow manure-seawater were relatively better than those from the reference media (BG11). Based on the current findings, it is concluded that the growth nutrients from integrated cow manure-seawater can wholly substitute for the BG11 without affecting cyanobacterial growth, thereby reducing the usage of expensive chemical growth media. Thus,The results of study help to enhance the biomass production of both salt-sensitive and salt-tolerant cyanobacteria for sustainable biofuel and biofertilizer production. Cyanobacterial biomass is important for biofuel and biofertilizer, however, biomass production requires expensive chemical growth nutrients. To address this issue, we explored the useof inexpensive growth nutrient media from an integrated manure-seawater system for cyanobacterial biomass production. Salt-tolerant cyanobacterial strain HSaC and salt-sensitive cyanobacterial strain LC were tested to evaluate the potential of integrated manure-seawater media for sustainable cyanobacterial biomass production. As a prerequisite for seawater experiments, strain HSaC was grown at different NaCl concentrations (0 mM, 60 mM, 120 mM, 180 mM, 240 mM and 300 mM) to identify the optimum salt concentration. The highest biomass yield and photosynthetic pigment contents were obtained at 120 mM NaCl concentration. The highest exo-polysaccharide (EPS) content was obtained at 180 mM NaCl concentration. The treatments for the manure-seawater media were cow manure, pig manure, chicken manure and BG11, each with distilled water, diluted seawater and non-diluted seawater. The highest biomass and photosynthetic pigment yield for cyanobacterial strains LC and HSaC were obtained from 0.5 dS/m and 10 dS/m diluted seawater integrated with cow manure, respectively, but pig and chicken manure performed poorly. Overall, the biomass production and photosynthetic pigment results from cow manure-seawater were relatively better than those from the reference media (BG11). Based on the current findings, it is concluded that the growth nutrients from integrated cow manure-seawater can wholly substitute for the BG11 without affecting cyanobacterial growth, thereby reducing the usage of expensive chemical growth media. Thus,The results of study help to enhance the biomass production of both salt-sensitive and salt-tolerant cyanobacteria for sustainable biofuel and biofertilizer production.

This data set is the experimental data set of maize agroecosystem adaptation to climate change from 2018 to 2021 in Gucheng Station. It mainly contains the interannual and annual variation data of growth period, biomass, leaf area index, photosynthetic physiology, spectral characteristics, soil moisture, grain filling rate and yield of the same variety of maize at different sowing dates. This dataset has great significance for the revision of agrometeorological business service index, the improvement and regional application of agrometeorological simulation model, and the development of agrometeorological applicable technology for the study of maize ecosystem adaptation to climate change. This data set is the experimental data set of maize agroecosystem adaptation to climate change from 2018 to 2021 in Gucheng Station. It mainly contains the interannual and annual variation data of growth period, biomass, leaf area index, photosynthetic physiology, spectral characteristics, soil moisture, grain filling rate and yield of the same variety of maize at different sowing dates. This dataset has great significance for the revision of agrometeorological business service index, the improvement and regional application of agrometeorological simulation model, and the development of agrometeorological applicable technology for the study of maize ecosystem adaptation to climate change.

As a basic properties of forest vegetation, forest succession law is the basis of understanding forest community, managing forest and utilizing forest rationally. Typical evergreen broad-leaved forest is a zonal vegetation in the subtropical area of east China. The existing vegetation is mostly in different secondary succession stages due to human and natural disturbance. Plant species composition is an important indicator of the long-term terrestrial ecosystem observation of National Ecosystem Research Network of China (CNERN). It affects the biogeochemical cycle, productivity, carbon sequestration, biodiversity and ecosystem services of forest ecosystems. According to CNERN monitoring standards, Zhejiang Tiantong Forest Ecosystem National Observation and Research Station finished three investigations at three succession plots and established a dataset on species composition during 2008 and 2017. The dataset included species name, abundance, mean diameter and biomass of woody plants in the plot. The species composition database provides critical data for in-depth studies of forest species diversity, structure and function under succession or environment change, and can support forest management and ecosystem service evaluation in this region. As a basic properties of forest vegetation, forest succession law is the basis of understanding forest community, managing forest and utilizing forest rationally. Typical evergreen broad-leaved forest is a zonal vegetation in the subtropical area of east China. The existing vegetation is mostly in different secondary succession stages due to human and natural disturbance. Plant species composition is an important indicator of the long-term terrestrial ecosystem observation of National Ecosystem Research Network of China (CNERN). It affects the biogeochemical cycle, productivity, carbon sequestration, biodiversity and ecosystem services of forest ecosystems. According to CNERN monitoring standards, Zhejiang Tiantong Forest Ecosystem National Observation and Research Station finished three investigations at three succession plots and established a dataset on species composition during 2008 and 2017. The dataset included species name, abundance, mean diameter and biomass of woody plants in the plot. The species composition database provides critical data for in-depth studies of forest species diversity, structure and function under succession or environment change, and can support forest management and ecosystem service evaluation in this region.

As a kind of important renewable resources, grassland resources have significant influence on human’s daily life. China is a country with abundant grassland resources. The scientific use of grassland resources would contribute to the sustainable development of animal husbandry, national unity and the stability of the country. However, grassland resources are facing with more and more problems, with the development of agriculture, industry, animal husbandry, population growth, and the impact of global warming. Therefore, obtaining accurate real-time information of the growth condition of grassland is quite important. People can use this information carrying on the scientific management of grassland resources, thus protecting grassland resources and keeping the sustainable development of animal husbandry. Traditional observation method is mainly ground experiment, which would cost lots of time and money. Remote sensing data has the advantage of near-real time, dynamic observation and contains image with large scale. But a single type of remote sensing data cannot meet the needs of high temporal-spatial grassland biomass observation. This study intends to use data fusion method to generate high temporal-spatial remote sensing data. Then combining with ground survey data , we established the parametric and non-parametric model. Eventually we developed the optimal aboveground biomass model for Qinghai Lake Basin and generate the biomass time series with 30 meter resolution and 8 day interval during 2000—2015. We then analyzed the grassland trend in Qinghai Lake basin during the past 16 years. The main work and the conclusions of our findings are as follows: (1) According to the actual situation of Qinghai Lake Basin, we developed the optimal fusion model from three prospects: the selection of generating synthetic NDVI, the comparison between different image (different MODIS product and TM image in different years), and the development of data fusion algorithm. We finally generated the synthetic NDVI series of Qinghai Lake Basin. The selection of the fusion scheme would directly affect the precision of the vegetation index, and then impact the accuracies of the construction of the biomass model. Based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm, we used MCD43A4 as the input MODIS file. We then chose data in the same year, in adjacent year, and data with 2-yr intervals. Based on the landcover type, we used decision tree to choose different windows for different vegetation types: 350m for croplands; 950m for forest; 750m for grassland and other vegetation types. We have synthetic NDVI time series with relatively high spatial and temporal resolution. It can tell more spatial details on the vegetation variation compared with MODIS data. (2) Based on the measured data and the fusion vegetation index data, the parametric models and a non-parametric model were established and compared. Finally, the experimental results show that the support vector machine (SVM) model has good accuracy. Based on this model, the data set of 30-m data series of grassland aboveground biomass in Qinghai Lake area in the past 16 years was established. We built the model in the following four steps: We generated the synthetic NDVI series with the optimal data fusion scheme; combined with the 291 field samples and vegetation index data, we generated the biomass estimation model of Qinghai Lake region; We chose the optimal model for biomass estimation according to the test data. We finally generated the biomass series with 320 scenes. Biomass estimation model with synthetic NDVI (r=0.85, RMSE=74.45g/m2) can not only maintain accuracies of the models based on MODIS NDVI (r=0.85, RMSE=73.20g/m2); it can also increase the spatial resolution of the biomass from 500m to 30m, and increase the time resolution up to 8 days. (3) The degradation condition of grassland in in Qinghai Lake area was analyzed. We found that during the past 16 years, grassland resources in this area have changed greatly. Grassland in the south lakeshore and the mountainous area in the northern part of the basin showed large degradation, while in the middle of the Qinghai Lake Basin, grassland showed growing tendencies. Grassland with apparent degradation accounted for 8.5% of the basin,while grassland with apparent growth account for 24.5% of the basin. The degradation of grassland were partly contributed by global warming; while the unscientific use of grassland resources is another critical issue caused the land degradation. In addition, as a tourist hot spot, In recent years, tourists number in Qinghai Lake Basin increased dramatically, which would also contribute to the grassland degradation in the local area. As a kind of important renewable resources, grassland resources have significant influence on human’s daily life. China is a country with abundant grassland resources. The scientific use of grassland resources would contribute to the sustainable development of animal husbandry, national unity and the stability of the country. However, grassland resources are facing with more and more problems, with the development of agriculture, industry, animal husbandry, population growth, and the impact of global warming. Therefore, obtaining accurate real-time information of the growth condition of grassland is quite important. People can use this information carrying on the scientific management of grassland resources, thus protecting grassland resources and keeping the sustainable development of animal husbandry. Traditional observation method is mainly ground experiment, which would cost lots of time and money. Remote sensing data has the advantage of near-real time, dynamic observation and contains image with large scale. But a single type of remote sensing data cannot meet the needs of high temporal-spatial grassland biomass observation. This study intends to use data fusion method to generate high temporal-spatial remote sensing data. Then combining with ground survey data , we established the parametric and non-parametric model. Eventually we developed the optimal aboveground biomass model for Qinghai Lake Basin and generate the biomass time series with 30 meter resolution and 8 day interval during 2000—2015. We then analyzed the grassland trend in Qinghai Lake basin during the past 16 years. The main work and the conclusions of our findings are as follows: (1) According to the actual situation of Qinghai Lake Basin, we developed the optimal fusion model from three prospects: the selection of generating synthetic NDVI, the comparison between different image (different MODIS product and TM image in different years), and the development of data fusion algorithm. We finally generated the synthetic NDVI series of Qinghai Lake Basin. The selection of the fusion scheme would directly affect the precision of the vegetation index, and then impact the accuracies of the construction of the biomass model. Based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm, we used MCD43A4 as the input MODIS file. We then chose data in the same year, in adjacent year, and data with 2-yr intervals. Based on the landcover type, we used decision tree to choose different windows for different vegetation types: 350m for croplands; 950m for forest; 750m for grassland and other vegetation types. We have synthetic NDVI time series with relatively high spatial and temporal resolution. It can tell more spatial details on the vegetation variation compared with MODIS data. (2) Based on the measured data and the fusion vegetation index data, the parametric models and a non-parametric model were established and compared. Finally, the experimental results show that the support vector machine (SVM) model has good accuracy. Based on this model, the data set of 30-m data series of grassland aboveground biomass in Qinghai Lake area in the past 16 years was established. We built the model in the following four steps: We generated the synthetic NDVI series with the optimal data fusion scheme; combined with the 291 field samples and vegetation index data, we generated the biomass estimation model of Qinghai Lake region; We chose the optimal model for biomass estimation according to the test data. We finally generated the biomass series with 320 scenes. Biomass estimation model with synthetic NDVI (r=0.85, RMSE=74.45g/m2) can not only maintain accuracies of the models based on MODIS NDVI (r=0.85, RMSE=73.20g/m2); it can also increase the spatial resolution of the biomass from 500m to 30m, and increase the time resolution up to 8 days. (3) The degradation condition of grassland in in Qinghai Lake area was analyzed. We found that during the past 16 years, grassland resources in this area have changed greatly. Grassland in the south lakeshore and the mountainous area in the northern part of the basin showed large degradation, while in the middle of the Qinghai Lake Basin, grassland showed growing tendencies. Grassland with apparent degradation accounted for 8.5% of the basin,while grassland with apparent growth account for 24.5% of the basin. The degradation of grassland were partly contributed by global warming; while the unscientific use of grassland resources is another critical issue caused the land degradation. In addition, as a tourist hot spot, In recent years, tourists number in Qinghai Lake Basin increased dramatically, which would also contribute to the grassland degradation in the local area.

The area and volume of Chinese fir plantation is the largest in China. Based on a long-time biomass production observation on the Chinese fir plantation comprehensive observation field by Huitong National Forest Ecosystem Research Station, Hunan (Huitong Station). The dataset integrated annual biomass production of the Chinese fir plantation in Huitong Station in the period of 2007–2020, which both comprised the dry weight of trunk, branches, leaves, fruits (flowers), bark and aerial roots. The establishment and sharing of this dataset mainly provides data support for the biomass production research of Chinese fir plantation under the background of global change. It is of great significance to deeply understand the structural and functional characteristics of Chinese fir plantation ecosystem and formulate reasonable management measures of Chinese fir plantation. The area and volume of Chinese fir plantation is the largest in China. Based on a long-time biomass production observation on the Chinese fir plantation comprehensive observation field by Huitong National Forest Ecosystem Research Station, Hunan (Huitong Station). The dataset integrated annual biomass production of the Chinese fir plantation in Huitong Station in the period of 2007–2020, which both comprised the dry weight of trunk, branches, leaves, fruits (flowers), bark and aerial roots. The establishment and sharing of this dataset mainly provides data support for the biomass production research of Chinese fir plantation under the background of global change. It is of great significance to deeply understand the structural and functional characteristics of Chinese fir plantation ecosystem and formulate reasonable management measures of Chinese fir plantation.

Silicon carbide (SiC) ceramics prepared by the conventional process has excellent properties and wide application prospects, but the increased cost of high-temperature preparation process restricts its further development. In contrast, the abundant porous structure of biomass makes itself to be ideal replacement of SiC ceramic prepared at low temperature. This paper reviewed the structure characteristics, preparation methods, pyrolysis mechanism and influence parameters of biomass-based SiC ceramic, and eventually explored the current problems and development trends of the pretreatment of carbon source and silicon source, the pyrolysis process and the application research on the preparation for biomass-based SiC ceramic.

With the popularity of large-scale MIMO and Internet of Things, energy efficiency(EE) optimization issues will be the challenges of the future development of 5G, in which multiple users reuse in the same sub channel shows more attractive. Assuming that the conditions of the channel state information are fully understood, the base station allocates the machine equipment with the largest allocation factor to the user equipment according to the maximum proportion allocation principle, in the uplink, a bilateral matching algorithm of one sub-channel and user equipment is adopted, and the EE is maximized through the power control and time scheduling based on NOMA. The simulation result shows that the energy consumption of the proposed optimization scheme is reduced by more than 6% compared with the existing schemes.

This dataset compiled solar radiation components data measured by horizontal and tilted radiometers and turbulent energy fluxes data from natural secondary forests at the Heilongjiang Maoershan Forest Ecosystem National Observation and Research Station for one consecutive year, including incident and reflected short-wave radiation, long-wave radiation, photosynthetically active radiation, near-infrared radiation, sensible heat flux, latent heat flux, and soil surface heat flux data, stored for 30 min time scale data files. The coverage of radiation data and turbulent energy flux data for the growing season (May 3 to October 10) was 100% and 81.3% respectively.Contents and meanings of the fields in the column labels of the Excel file of this dataset: SR (shortwave radiation) , LR (longwave radiation), Rn (net radiation), PAR (photosynthetically active radiation), NIR (near-infrared radiation), Tc (temperature measured by the built-in temperature sensor of the radiation meter), _dn (incident), _up (reflected), _net (net of incident minus reflected), _hon ( data collected from horizontally mounted radiation meter), _slope (data collected from tilted mounted radiation meter), _corr (corrected), _r (albedo), G_mean (heat flux measured by soil heat flux plate), Ss (soil heat storage above the heat flux plate), G0 (soil heat flux), H (sensible heat flux), LE (latent heat flux). For example, a column labeled SR_dn_slope_corr means that the column is the corrected data of the tilt-mounted radiation meter measuring incident short-wave radiation. This dataset compiled solar radiation components data measured by horizontal and tilted radiometers and turbulent energy fluxes data from natural secondary forests at the Heilongjiang Maoershan Forest Ecosystem National Observation and Research Station for one consecutive year, including incident and reflected short-wave radiation, long-wave radiation, photosynthetically active radiation, near-infrared radiation, sensible heat flux, latent heat flux, and soil surface heat flux data, stored for 30 min time scale data files. The coverage of radiation data and turbulent energy flux data for the growing season (May 3 to October 10) was 100% and 81.3% respectively.Contents and meanings of the fields in the column labels of the Excel file of this dataset: SR (shortwave radiation) , LR (longwave radiation), Rn (net radiation), PAR (photosynthetically active radiation), NIR (near-infrared radiation), Tc (temperature measured by the built-in temperature sensor of the radiation meter), _dn (incident), _up (reflected), _net (net of incident minus reflected), _hon ( data collected from horizontally mounted radiation meter), _slope (data collected from tilted mounted radiation meter), _corr (corrected), _r (albedo), G_mean (heat flux measured by soil heat flux plate), Ss (soil heat storage above the heat flux plate), G0 (soil heat flux), H (sensible heat flux), LE (latent heat flux). For example, a column labeled SR_dn_slope_corr means that the column is the corrected data of the tilt-mounted radiation meter measuring incident short-wave radiation.

Species composition plays an important role in the nutrient circulation, forest productivity formation, carbon storing, biodiversity conservation and ecological function maintenance of forest ecosystems. It is an important community indicator of the terrestrial ecosystems of Chinese Ecosystem Research Network (CERN) and National Ecosystem Research Network of China (CNERN). The monsoon evergreen broad-leaved forest (MEBF) is the climax vegetation type in the subtropics of China, which is sensitive to environment change. According to field investigation of the MEBF monitoring plot in Dinghushan Forest Ecosystem Research Station that is one of the stations of CERN and CNERN, the species composition of woody plants with diameter at breast height of ≥1 cm from the year of 1992 to 2015 were recorded. The data comprised the scientific name of species, abundance, biomass and importance value of the plot. The setup and data sharing of this MEBF species composition database provides critical data for in-depth studies of forest species composition, structure and function under environment change, supporting forest management and ecosystem service evaluation in this region. Species composition plays an important role in the nutrient circulation, forest productivity formation, carbon storing, biodiversity conservation and ecological function maintenance of forest ecosystems. It is an important community indicator of the terrestrial ecosystems of Chinese Ecosystem Research Network (CERN) and National Ecosystem Research Network of China (CNERN). The monsoon evergreen broad-leaved forest (MEBF) is the climax vegetation type in the subtropics of China, which is sensitive to environment change. According to field investigation of the MEBF monitoring plot in Dinghushan Forest Ecosystem Research Station that is one of the stations of CERN and CNERN, the species composition of woody plants with diameter at breast height of ≥1 cm from the year of 1992 to 2015 were recorded. The data comprised the scientific name of species, abundance, biomass and importance value of the plot. The setup and data sharing of this MEBF species composition database provides critical data for in-depth studies of forest species composition, structure and function under environment change, supporting forest management and ecosystem service evaluation in this region.

With waste walnut shell as raw material, biomass based porous active carbon was made by microwave oven method. The effects of microwave power, activation time and mass fraction of phosphoric acid on adsorptive property of biomass based porous active carbon in the process of physical activation of active carbon precursor were studied by response surface method and numerical simulation method, the preparation plan of biomass based porous active carbon was optimized, and the optimal biomass based porous active carbon property was characterized. The results show that three factors affect the adsorptive property of biomass based porous active carbon, but the effect of microwave power is obviously more significant than that of mass fraction of phosphoric acid, and the effect of mass fraction of phosphoric acid is more significant than that of activation time. The optimized preparation conditions are:microwave power is 746W, activation time is 11.2min and mass fraction of phosphoric acid is 85.9% in the process of physical activation of activated carbon precursor by microwave heating method. For the optimal biomass based porous active carbon, the adsorption value of iodine is 1074.57mg/g, adsorption value of methylene blue is 294.4mL/g and gain rate is 52.1%.