Modeling dynamic assessment of ecosystem services based on remote sensing technology: A sampling of the Gansu grassland ecosystem

The ecosystem is important because it is the life sustaining system for human survival.Three ecosystem characteristics are:regional particularities,ecosystem complexity and conventional cultural particularities.This paper develops a remote sensing based dynamic model to assess grassland ecosystem service values involving multidisciplinary knowledge.The ecological value of grassland ecosystems is focused on using a remote sensing technique in the model,and setting up the framework for a dynamic assessing model.The grassland ecological services condition and value in 1985 is used as the benchmark.The dynamic model has two adjusting indicators:biomass and price index.The biomass is simulated using the CASA(Carnegie-Ames-Stanford Approach) model.The price index was obtained from statistics data published by the statistical bureau.Results show that the grassland ecosystem value in Gansu Province was 28.36 billion Chinese Yuan in 1985,140.37 billion in 1999 and 130.86 billion in 2002.     

Cd(II), Pb(II) and Zn(II) Removal from Contaminated Water by Biosorption Using Activated Sludge Biomass

Biosorption using activated sludge biomass (ASB) as a potentially sustainable technology for the treatment of wastewater containing different metal ions (Cd(II), Pb(II) and Zn(II)) was investigated. ASB metal uptake clearly competed with protons consumed by microbial biomass compared with control tests with non‐activated sludge biomass. Biosorption tests confirmed maximum exchange between metal ions and protons at pH 2.0–4.5. It was revealed by the study that the amount of metal ions released from the biomass increased with biomass sludge concentration. The result showed that maximum absorption of metal ions was observed for Cd(II) at pH 3.5, Pb(II) at pH 4.0, and pH 4.5 for Zn(II) ions. The maximum absorption capacities of ASB for Cd(II), Pb(II) and Zn(II) were determined to be 59.3, 68.5 and 86.5%, respectively. The biosorption of heavy metals was directly proportional to ASB stabilization corresponding to a reduction in heavy metals in the order of Cd < Pb < Zn. The order of increase of biosorption of metal ions in ASB was Zn(II) < Pb(II) < Cd(II), and this was opposite to that of non active sludge. The results indicate that ASB is a sustainable tools for the bioremediation of Cd(II), Pb(II) and Zn(II) ions from industrial sludge and wastewater treatment plants.     

The Effects of Hydrophilic Polymer and Soil Salinity on Corn Growth in Sandy and Loamy Soils

The interaction effects of different applied ratios of a hydrophilic polymer (Superab A200) (0, 0.2, 0.6% w/w) under various soil salinity levels (initial salinity, 4 and 8 ms/cm) were evaluated on available water content (AWC), biomass, and water use efficiency for corn grown in loamy sand and sandy clay loam soils. The results showed that the highest AWC was measured at the lowest soil salinity. The application of 0.6% w/w of the polymer at the lowest salinity level increased the AWC by 2.2 and 1.2 times greater than those of control in the loamy sand and sandy clay loam soils, respectively. The analysis of variance of data showed that the effect of salinity was significant on biomass and water use efficiency of corn in the loamy sand and sandy clay loam soils. The highest amounts of these traits were measured in soils with the lowest salinity level. Application of polymer at the rate of 0.6% in the loamy sand soil and at the rate of 0.2% in the sandy clay loam soil resulted in the highest aerial and root biomass and water use efficiency for corn. At these polymer rates the amounts of water use efficiency for corn were 2.6 and 1.7 times greater than those of control in the loamy sand and sandy clay loam soils, respectively. Thus, the use of hydrophilic polymer in soils especially in the sandy soils increases soil water holding capacity, yield, and water use efficiency of plant. On the other hand, decreases the negative effect of soil salinity on plant and helps for irrigation projects to succeed in arid and semi‐arid areas.     

分形插值与传统插值相结合的方法研究

本文针对测量数据提出了基于分形的线性插值 ,该法综合了分形插值理论和传统线性插值理论的优点。算例表明 :该法优于单一的分形插值和单一的线形插值 ,后验差检验法表明插值结果优等     

中国生物质能产业发展面临的矛盾和挑战

本文在分析中国生物质能产业发展现状的基础上,认为当前中国生物质能产业发展受到资源家底不清、政策环境不完善和生态保护压力等因素制约,存在着产业发展总体水平低,产业体系不健全,生产成本高,产业技术水平低,行业发展无序等问题。结合这些问题,立足我国资源环境承载能力,适应市场需求变化和科技进步趋势,本文提出今后重点加强生物质能产业发展政策扶持,生物质能原料资源调查评估,生物质能转化利用技术开发和能源产业示范基地建设,以期促进生物质能产业健康快速发展。     

The aboveground biomass of desert steppe and its spatiotemporal variation in western Inner Mongolia

A precise understanding of the aboveground biomass of desert steppe and its spatio-temporal variation is important to understand how arid ecosystems respond to climate change and to ensure that scarce grassland resources are used rationally. On the basis of 756 ground survey quadrats sampled in western Inner Mongolia steppe in 2005–2011 and remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS)—the normalized difference vegetation index (NDVI) dataset for the period of 2001–2011—we developed a statistical model to estimate the aboveground biomass of the desert steppe and further explored the relationships between aboveground biomass and climate factors. The conclusions are as follows: (1) the aboveground biomass of the steppe in the research area was 5.27 Tg (1 Tg=10¹² g) on average over 11 years; between 2001 and 2011, the aboveground biomass of the western Inner Mongolia steppe exhibited fluctuations, with no significant trend over time; (2) the aboveground biomass of the steppe in the research area exhibits distinct spatial variation and generally decreases gradually from southeast to northwest; and (3) the important factor causing interannual variations in aboveground biomass is precipitation during the period from January to July, but we did not find a significant relationship between the aboveground biomass and the corresponding temperature changes. The precipitation in this period is also an important factor influencing the spatial distribution of aboveground biomass (R²=0.39, P<0.001), while the temperature might be a minor factor (R²=0.12, P<0.01). The uncertainties in our estimate are primarily due to uncertainty in converting the fresh grass yield estimates to dry weight, underestimates of the biomass of shrubs, and error in remote sensing dataset.     

Biosorption of Heavy Metals in Leachate Derived from Gold Mine Tailings Using Aspergillus fumigatus

Leachate derived from bioleaching process contains high amount of metals that must be removed before discharging the water. Aspergillus fumigatus was isolated from a gold mine tailings and its ability to remove of As, Fe, Mn, Pb, and Zn from aqueous solutions and leachate of bioleaching processes was assessed. Batch sorption experiments were carried out to characterize the capability of fungal biomass (FB) and iron coated fungal biomass (ICFB) to remove metal ions in single and multi‐solute systems. The maximum sorption capacity of FB for As(III), As(V), Fe, Mn, Pb, and Zn were 11.2, 8.57, 94.33, 53.47, 43.66, and 70.4 mg/g, respectively, at pH 6. For ICFB, these values were 88.5, 81.3, 98.03, 66.2, 50.25, and 74.07 mg/g. Results showed that only ICFB was found to be more effective in removing metal ions from the leachate. The amount of adsorbed metals from the leachate was 2.88, 21.20, 1.91, 0.1, and 0.08 mg/g for As, Fe, Mn, Zn, and Pb, respectively. The FT‐IR analysis showed involvement of the functional groups of the FB in the metal ions sorption. Scanning electron microscopy revealed that surface morphological changed following metal ions adsorption. The study showed that the indigenous fungus A. fumigatus was able to remove As, Fe, Mn, Pb, and Zn from the leachate of gold mine tailings and therefore the potential for removing metal ions from metal‐bearing leachate.     

Plant Species Richness Increased Belowground Plant Biomass and Substrate Nitrogen Removal in a Constructed Wetland

The effects of plant species richness on both above‐ and belowground plant biomass, plant nitrogen (N) pool size, and substrate N concentrations were studied in a full‐scale subsurface vertical‐flow constructed wetland (CW). Results showed that (i) plant species richness increased belowground plant biomass and its N pool size but had no effect on aboveground plant biomass and its N pool size; (ii) plant species richness increased substrate N removal, especially ammonium N removal; and (iii) plant species richness had no effect on plant N use efficiency, suggesting that the N pool size increased with increasing plant species richness. More N accumulation could be removed through harvesting plant biomass. We concluded that the N removal performance of the CW improved by plant species richness through increasing belowground biomass and relevant N pool size.     

Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation

Abstract

While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial–temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (R ² of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail.