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\"The increasing importance of biomass as a renewable energy source has led to an acute need for reliable and detailed information on its assessment, consumption and supply. Responding to this need, and overcoming the lack of standardised measurement and accounting procedures, this best-selling handbook provides the reader with the skills to understand the biomass resource base, the tools to assess the resource, and explores the pros and cons of exploitation. This new edition has been fully updated and revised with new chapters on sustainability methodologies. Topics covered include assessment methods for woody and herbaceous biomass, biomass supply and consumption, remote sensing techniques, food security, sustainability and certification as well as vital policy issues. The book includes international case studies on techniques from measuring tree volume to transporting biomass, which help to illustrate step-by-step methods. Technical appendices offer a glossary of terms, energy units and other valuable resource data. \"-- Provided by publisher.

Most grazing lands in Mediterranean ecosystems that support extensive sheep farming systems are characterized by unfavorable edapho-climatic conditions, especially in semi-arid areas. Often, though, their use is far from sustainable, causing erosion and ecosystem degradation impacts. In this paper, we explore the use, productivity, and flora diversity of typical Mediterranean grazing lands in four farms at the Agra locality in the western part of Lesvos Island, Greece. For a period of two consecutive growing seasons (September to June), we recorded herbage biomass on 16 plots of grazing lands with three measurements per season of land cover and plant productivity (biomass) inside small exclosures (cages) protected from grazing. We recorded the species richness of herbaceous plant communities within and outside the cages at the end of every growing season, the period of maximum growth of herbaceous species. We also chemically analyzed the biomass for crude protein at the end of each season. Results show sizable productivity differences among pasture plots as well as seasons and an overall medium to high degree of productivity and species richness considering the relatively intensive grazing, with little differences over the different cages and the degree of grazing intensity. These results suggest that the “history” of the fields is important, as grazing lands that had been used for arable crops in the past, as well as those leveled and in favorable locations, were the most productive and diverse ones, while shallower soils and inclined grazing lands showed signs of overuse and degradation. Overall, though, these ecosystems showed a high degree of resilience despite their intensive use.

Ratoon rice production has been an emerging cropping system to increase food quality and productivity worldwide. Efficient monitoring of ratoon rice aboveground biomass (AGB) over large areas is valuable for precision agriculture, as AGB is closely related to crop grain yield and quality. Unmanned aerial vehicle (UAV) remote sensing has opened an unprecedented opportunity to efficiently monitor crop AGB instead of labor-intensive ground measurements. Vegetation indices (VIs)-based approach for estimating AGB is easily affected by background materials and often suffers saturation problems at high AGB levels. Although the combined use of UAV-collected structural and spectral features can alleviate these problems to some extent, uncertainties on the AGB estimation still exist for crop with significant difference in canopy architecture and AGB composition throughout the growing season (e.g., ratoon rice). There is a hypothesis that the combination of spectral, textural and structural features can improve ratoon rice AGB estimations across different developmental stages. Therefore, the utility of UAV-based spectral, textural and structural features were quantified in estimating ratoon rice AGB at field level with contrasting agronomic treatments (i.e., nitrogen fertilizer and stubble height), in which multiple linear regression (MLR) and gaussian process regression (GPR) methods were applied and compared. Results showed that (1) each feature had its own respective limitation: specifically, spectral and textural features exhibited insufficient sensitivity to AGB variability of remaining stubbles or stem at early stage and suffered saturation problem at grain filling stage; structural features were difficult to detect the emergence of panicles from panicle initiation to heading stages; (2) the combination of three types of features can complement each other and achieved the highest accuracy using GPR method: the combination of spectral, structural and textural features achieved the best estimation accuracy for estimating ratoon rice AGB with an R2 of 0.94 and RMSE of 81.4 g m−2 across different developmental stages, which significantly improved the model performance compared to the combination of spectral and textural features (R2 = 0.56, RMSE = 170.2 g m−2) and the combination of spectral and structural features (R2 = 0.86, RMSE = 138.8 g m−2). In summary, this study provides a novel approach for efficiently estimating ratoon rice AGB at field level, which is critical for timely decision making (e.g., determine when and where to apply fertilizer or pesticide) in precision agriculture.

Traditional carriers play a major role in wastewater treatment worldwide due to their reliability, ease of production, well-established analytical methods, and strong treatment performance. Recent studies indicate that polyvinyl-alcohol-based microcarriers may surpass conventional media, as their smaller size, higher porosity, and increased specific surface area enable them to retain substantially more biomass within reactors. However, their practical application remains limited because fewer analytical methods and studies exist for these materials, largely due to their small dimensions and heat sensitivity, and their behaviour under industrial conditions—including their kinetics—has yet to be fully characterised and validated. This study aims to address these gaps by reviewing existing biomass measurement standards and highlighting their limitations when applied to microcarriers and by proposing alternative experimental approaches better suited for evaluating biomass on such sensitive yet high-capacity carriers. We present a set of experimental methods (still subject to further refinement) that demonstrate reliable performance with these materials, and to validate our approach, we quantified biomass in both in vitro systems and containerised-scale technologies, reaching up to 14 kg/m3 during winter and 8.7 kg/m3 in spring. Laboratory-scale experiments showed that both heterotrophic and autotrophic cultures can achieve high biomass levels of up to 21 kg/m3 and 16 kg/m3, respectively. Heterotrophs exhibited lower growth inhibition under shear stress, while autotrophs displayed a distinct shear-force niche around 0.09 µN within the reactor.

Estimation of the amount of carbon stored in forests is a key challenge for understanding the global carbon cycle, one which remote sensing is expected to help address. However, estimation of carbon storage in moderate to high biomass forests is difficult for conventional optical and radar sensors. Lidar (light detection and ranging) instruments measure the vertical structure of forests and thus hold great promise for remotely sensing the quantity and spatial organization of forest biomass. In this study, we compare the relationships between lidar-measured canopy structure and coincident field measurements of above-ground biomass at sites in the temperate deciduous, temperate coniferous, and boreal coniferous biomes. A single regression for all three sites is compared with equations derived for each site individually. The single equation explains 84% of variance in above-ground biomass (P < 0.0001) and shows no statistically significant bias in its predictions for any individual site.

Entomological radar is a specially designed instrument that can measure the behavioral and biological characteristics of high-altitude migrating insects. Its application is of great significance for the monitoring, early warning, and control of agricultural pests. As an important component of the local migratory biomass, insects fly in the air during the day and night. The fully polarimetric entomological radar was carefully designed with all-day, all-weather, and multi-function measurement capabilities. The fully polarimetric entomological radar measures the mass of a single insect based on the radar cross-sectional (RCS) measurement and then calculates the biomass of migrating insects. Therefore, the measurement accuracy of the insect RCS is the key indicator affecting the accuracy of migratory biomass statistics. Due to the radar’s lack of in-beam angle measurement ability, the insect RCS is usually measured based on the assumption that the insect is on the beam center. Therefore, the measured RCS will be smaller than true value if the insect deviates from the beam center due to the gain curve of the antenna. This leads to measurement errors in regard to the insect mass and migratory biomass. In order to solve this problem, a biomass estimation method, reported in this paper, was designed under the assumption of a uniform distribution of migrating insects in the radar monitoring airspace. This method can estimate the individual RCS expectation of migrating insects through a statistical method without measuring the position of the insects in the beam and then obtain the migratory biomass. The effectiveness of the model and algorithm is verified by simulations and entomological radar field measurements.

Accurate real-time process control is necessary to increase process efficiency, and optical sensors offer a competitive solution because they provide diverse system information in a noninvasive manner. We used an innovative scattered light sensor for the online monitoring of biomass during lactic acid production in a membrane bioreactor system because biomass determines productivity in this type of process. The upper limit of the measurement range in fermentation broth containing Bacillus coagulans was ~2.2 g·L−1. The specific cell growth rate (µ) during the exponential phase was calculated using data representing the linear range (cell density ≤ 0.5 g·L−1). The results were consistently and reproducibly more accurate than offline measurements of optical density and cell dry weight, because more data were gathered in real-time over a shorter duration. Furthermore, µmax was measured under different filtration conditions (transmembrane pressure 0.3–1.2 bar, crossflow velocity 0.5–1.5 m·s−1), showing that energy input had no significant impact on cell growth. Cell density was monitored using the sensor during filtration and was maintained at a constant level by feeding with glucose according to the fermentation kinetics. Our novel sensor is therefore suitable for integration into control strategies for continuous fermentation in membrane bioreactor systems.

Pathogenic microorganisms and viruses can easily transfer from one host to another and cause disease in humans. The determination of these pathogens in a time- and cost-effective way is an extreme challenge for researchers. Rapid and label-free detection of pathogenic microorganisms and viruses is critical in ensuring rapid and appropriate treatment. Sensor technologies have shown considerable advancements in viral diagnostics, demonstrating their great potential for being fast and sensitive detection platforms. In this review, we present a summary of the use of an interferometric reflectance imaging sensor (IRIS) for the detection of microorganisms. We highlight low magnification modality of IRIS as an ensemble biomolecular mass measurement technique and high magnification modality for the digital detection of individual nanoparticles and viruses. We discuss the two different modalities of IRIS and their applications in the sensitive detection of microorganisms and viruses.

Synthetic aperture radar (SAR) imaging systems derive microwave data, from space or airborne (piloted and remote piloted), that provide opportunities for the interpretation of many characteristics of the terrain surface. The increasing number of satellites equipped with SAR data acquisition systems that are being launched with a range of wavelengths, polarizations, and operating characteristics are enabling a better understanding of the earth’s environment, for such activities as vegetation analysis, forest inventories, land subsidence, and urban analysis. In addition, airborne systems for remote piloted systems and ground-based systems are available. This Special Issue presents six quality scientific papers on typical applications of SAR technologies. They include methods for the determination of above ground biomass (AGB), crop mapping using data from an advanced X-band system developed in Japan, analysis of natural and human-induced slow-rate ground deformations in the region of Campania, in Italy, the location of landslides caused by natural phenomena based on SAR images derived from the Japanese high-resolution Advanced Land Observing Satellite-2 (ALOS-2), and monitoring the size of refugee camps and their environmental impacts caused by the displacement of people from Myanmar to the Cox’s Bazar District, around Kutupalong, in Bangladesh. The paper concludes with some comments on the future directions of developments in SAR systems.

Low-frequency space-based synthetic aperture radar (SAR) is an ideal sensor for measuring forest biomass, but can suffer from ionospheric effects. The variation in total electron content (TEC), originating from ionospheric turbulence, causes the along track point spread function (PSF) to degrade in a manner which depends on ionospheric conditions. In this study, the effect of this PSF on the single point statistics (probability density function) and two point statistics (autocorrelation function (ACF)) is derived. It is shown that the K-distribution order parameter is directly proportional to the ionospheric turbulence, as quantified by CkL. The complex ACF is a measure of amplitude scintillation, and the intensity ACF is a measure of both the order parameter and the terrain correlation length. A simulation is performed which clearly shows that measuring the order parameter ratio between ionospherically disturbed and undisturbed images is a measure of CkL. This measure can be used two orders of magnitude below the point where the ionosphere causes defocusing of the SAR image. It is concluded that the usefulness of this new measure can only be verified by experimental data since the temporal stability of the underlying order parameter is unknown.