Preliminary Investigation on Phytoplankton Dynamics and Primary Production Models in an Oligotrophic Lake from Remote Sensing Measurements
Ilaria CesanaMariano BrescianiSergio CogliatiClaudia GiardinoRemika GupanaDario MancaStefano SantabarbaraMonica PinardiMartina AustoniAndrea LamiRoberto Colombo
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The aim of this study is to test a series of methods relying on hyperspectral measurements to characterize phytoplankton in clear lake waters. The phytoplankton temporal evolutions were analyzed exploiting remote sensed indices and metrics linked to the amount of light reaching the target (EPAR), the chlorophyll-a concentration ([Chl-a]OC4) and the fluorescence emission proxy. The latter one evaluated by an adapted version of the Fluorescence Line Height algorithm (FFLH). A peculiar trend was observed around the solar noon during the clear sky days. It is characterized by a drop of the FFLH metric and the [Chl-a]OC4 index. In addition to remote sensed parameters, water samples were also collected and analyzed to characterize the water body and to evaluate the in-situ fluorescence (FF) and absorbed light (FA). The relations between the remote sensed quantities and the in-situ values were employed to develop and test several phytoplankton primary production (PP) models. Promising results were achieved replacing the FA by the EPAR or FFLH in the equation evaluating a PP proxy (R2 > 0.65). This study represents a preliminary outcome supporting the PP monitoring in inland waters by means of remote sensing-based indices and fluorescence metrics.Keywords:
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In this paper, a laboratory-based hyperspectral imaging system is used to acquire hyperspectral data cubes from different algae samples of known mixtures. The data are obtained under controlled and repeatable conditions. Hyperspectral image processing is complicated by the size of the corresponding datasets so hyperspectral image pre-processing techniques such as dimensionality reduction are necessary before spectral analysis. We assessed hyperspectral response of mixed algal cultures containing two algae types to characterize the laboratory-based hyperspectral imaging system. Changes in the hyper spectral imaging system's response to variations in volume and combinations of algae concentrations were tested. Preliminary results demonstrate the system's capability to differentiate algal species, concentrations and sample volumes.
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This paper briefly introduces applications of FT-IR hyperspectral imaging to laboratory analyses and focuses on application of FT-IR/ATR imaging to crop protection products on test and plant surfaces. Results from univariate and multivariate analyses of the hyperspectral images are shown and advantages of the multivariate approach demonstrated.
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Introduction: To address boarding in hospital emergency departments, discharge-by-noon could free up inpatient beds earlier in the day. However, discharging all patients by noon can heavily burden inpatient units and may not be feasible. In this study, we determine the number of discharges after which the benefits of an additional discharge-by-noon diminish.
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Hyperspectral remote sensing data,compared with wide band remote sensing data, has the advantage of high spectral resolution.In this paper, the methods of hyperspectral remote sensing information processing, spectral analysis and its application in detecting the vegetation growth state, estimating the canopy biochemical characteristics and productivity of rice from vegetation hyperspectral remote sensing data are reviewed in detail. An detailed review on recent works about the integration of multiangle remote sensing and hyperspectral remote sensing data to construct the bidirectional reflectance distribution function model and then invert vegetation parameters accurately are given. Finally the paper discuss the way of further application of hyperspectral remote sensing information in order to promote quanlifing remote sensing.
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Abstract Hyperspectral analysis of vegetation involves obtaining spectral reflectance measurements in hundreds of bands in the electromagnetic spectrum. These measurements may be obtained using hand‐held spectroradiometers or hyperspectral remote sensing instruments placed onboard aircraft or satellites. Hyperspectral remote sensing provides valuable information about vegetation type, leaf area index, biomass, chlorophyll, and leaf nutrient concentration which are used to understand ecosystem functions, vegetation growth, and nutrient cycling. This article first reviews hyperspectral remote sensing and then describes current modeling and classification techniques used to estimate and predict vegetation type and biophysical characteristics.
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Hyperspectral imaging technology has been broadly applied in remote sensing because it collects echoed signals from across the electromagnetic (EM) spectrum and provides fruitfully helpful information. However, the processing or transformation of high-data-volume hyperspectral images, also viewed as snapshots varying with the EM spectrum, burdens the hardware resources, especially for the high spectral resolution and spatial resolution cases. To tackle this challenge, a novel reduced-order method based on the dynamic mode decomposition (DMD) algorithm is presented here to analyze hyperspectral images. The method decomposes the spatial-spectral hyperspectral images in terms of spatial dynamic modes and corresponding spectral patterns. Then, these spatial-spectral patterns are utilized to recover the raw hyperspectral images. Our proposed approach is benchmarked by the actual hyperspectral images measured at the Salinas scene. It is demonstrated that the proposed approach can represent the hyperspectral images with a low-rank model in spectral dimension. Our proposed approach could provide a useful tool for the model order reduction of hyperspectral images.
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The purpose of this study is to develop a worthwhile noon-hour program of intramural activities for the St. John High School. By making full use of the available facilities, the interested staff members, and the noon-hour, a program will be developed to give most of the students an opportunity to participate in a number of varied activities.
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The development of hyperspectral remote sensing has been conducted in the middle 1980s. Since then a number of hyperspectral imagers has been developed. The spectral response of it has covered the visible, near infrared, shortwave infrared and thermal infrared regions. In some time of the technical developments, the applications in various areas have also been conducted. For mineral exploration, the difference minerals as well as the alteration zones have been analyzed and extracted. Hyperspectral imagery enabled the detailed classification of vegetation. In case of study of wetland environment, some subspecies of wetland vegetation have been distinguished. Based on the spectral characteristics of vegetation and some artificial objects the urban environment and mapping have been tested by using of the hyperspectral remote sensing integrated with high spatial resolution data. In the studies the thermal infrared multispectral remote sensing has also been developed and dealt with the geological application.
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