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Process monitoring and control are fundamental to all processes; this holds especially for bioprocesses, due to their complex nature. Usually, bioprocesses deal with living cells, which have their own regulatory systems. It helps to adjust the cell to its environmental condition. This must not be the optimal condition that the cell needs to produce whatever is desired. Therefore, a close monitoring of the cell and its environment is essential to provide optimal conditions for production. Without measurement, no information of the current process state is obtained. In this book, methods and techniques are provided for the monitoring and control of bioprocesses. From new developments for sensors, the application of spectroscopy and modelling approaches, the estimation and observer implementation for ethanol production and the development and scale-up of various bioprocesses and their closed loop control information are presented. The processes discussed here are very diverse. The major applications are cultivation processes, where microorganisms were grown, but also an incubation process of bird’s eggs, as well as an indoor climate control for humans, will be discussed. Altogether, in 12 chapters, nine original research papers and three reviews are presented.

bioenvironmental control --- model-predictive controller --- zonal controlling --- dynamic modelling --- flotation --- bioleaching --- frother --- mixed culture --- machine learning --- Raman spectroscopy --- downstream processing --- chromatography --- flow cell --- extended Kalman filter --- biotechnological processes --- bioreactor control --- specific growth rate control --- batch-to-batch reproducibility --- thermal sensation --- thermal comfort --- machine-learning --- prediction --- adaptive controlling --- thermal growth curve --- temperature modeling --- thermoregulation --- monitoring and control --- bioprocess engineering --- calorimetry --- biocontrol agent --- Bacillus subtilis natto --- isolation --- molecular identification --- medium optimization --- antimicrobial activity --- bactericides --- spectral analyses --- biological selenate reduction --- electron donor competition --- nitrate --- perchlorate --- sequencing batch --- Pseudomonas --- siderophores --- antagonism --- batch fermentation --- exponential fed-batch fermentation --- bio-friendly formulations --- biocontrol --- nonlinear state estimation --- geometric observer --- bioreactor --- continuous system --- model-based sensor --- well-defined macromolecules --- sequence-defined macromolecules --- sequence-defined polymers --- conjugated oligomers --- oligo(arylene ethynylene)s --- biosensors --- sensors --- process monitoring --- bioprocess monitoring and control --- signal noise management --- dielectric spectroscopy --- PAT --- microbial bioprocessing --- n/a

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Monitoring of vegetation structure and functioning is critical to modeling terrestrial ecosystems and energy cycles. In particular, leaf area index (LAI) is an important structural property of vegetation used in many land surface vegetation, climate, and crop production models. Canopy structure (LAI, fCover, plant height, and biomass) and biochemical parameters (leaf pigmentation and water content) directly influence the radiative transfer process of sunlight in vegetation, determining the amount of radiation measured by passive sensors in the visible and infrared portions of the electromagnetic spectrum. Optical remote sensing (RS) methods build relationships exploiting in situ measurements and/or as outputs of physical canopy radiative transfer models. The increased availability of passive (radar and LiDAR) RS data has fostered their use in many applications for the analysis of land surface properties and processes, thanks also to their insensitivity to weather conditions and the capability to exploit rich structural and textural information. Data fusion and multi-sensor integration techniques are pressing topics to fully exploit the information conveyed by both optical and microwave bands.

artificial neural network --- downscaling --- simulation --- 3D point cloud --- European beech --- consistency --- adaptive threshold --- evaluation --- photosynthesis --- geographic information system --- P-band PolInSAR --- validation --- density-based clustering --- structure from motion (SfM) --- EPIC --- Tanzania --- signal attenuation --- trunk --- canopy closure --- REDD+ --- unmanned aerial vehicle (UAV) --- forest --- recursive feature elimination --- Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) --- aboveground biomass --- random forest --- uncertainty --- household survey --- spectral information --- forests biomass --- root biomass --- biomass --- unmanned aerial vehicle --- Brazilian Amazon --- VIIRS --- global positioning system --- LAI --- photochemical reflectance index (PRI) --- allometric scaling and resource limitation --- R690/R630 --- modelling aboveground biomass --- leaf area index --- forest degradation --- spectral analyses --- terrestrial laser scanning --- BAAPA --- leaf area index (LAI) --- stem volume estimation --- tomographic profiles --- polarization coherence tomography (PCT) --- canopy gap fraction --- automated classification --- HemiView --- remote sensing --- multisource remote sensing --- Pléiades imagery --- photogrammetric point cloud --- farm types --- terrestrial LiDAR --- altitude --- RapidEye --- forest aboveground biomass --- recovery --- southern U.S. forests --- NDVI --- machine-learning --- conifer forest --- satellite --- chlorophyll fluorescence (ChlF) --- tree heights --- phenology --- point cloud --- local maxima --- clumping index --- MODIS --- digital aerial photograph --- Mediterranean --- hemispherical sky-oriented photo --- managed temperate coniferous forests --- fixed tree window size --- drought --- GLAS --- smartphone-based method --- forest above ground biomass (AGB) --- forest inventory --- over and understory cover --- sampling design