The analysis of hyperspectral images is an important task in Remote Sensing. Foregoing radiometric calibration results in the assignment of incident electromagnetic radiation to digital numbers and reduces the striping caused by slightly different responses of the pixel detectors. However, due to uncertainties in the calibration some striping remains. This publication presents a new reduction framework that efficiently reduces linear and nonlinear miscalibrations by an image-driven, radiometric recalibration and rescaling. The proposed framework—Reduction Of Miscalibration Effects (ROME)—considering spectral and spatial probability distributions, is constrained by specific minimisation and maximisation principles and incorporates image processing techniques such as Minkowski metrics and convolution. To objectively evaluate the performance of the new approach, the technique was applied to a variety of commonly used image examples and to one simulated and miscalibrated EnMAP (Environmental Mapping and Analysis Program) scene. Other examples consist of miscalibrated AISA/Eagle VNIR (Visible and Near Infrared) and Hawk SWIR (Short Wave Infrared) scenes of rural areas of the region Fichtwald in Germany and Hyperion scenes of the Jalal-Abad district in Southern Kyrgyzstan. Recovery rates of approximately 97% for linear and approximately 94% for nonlinear miscalibrated data were achieved, clearly demonstrating the benefits of the new approach and its potential for broad applicability to miscalibrated pushbroom sensor data.
Sustained observations are required to determine the marine plastic debris mass balance and to support effective policy for planning remedial action. However, observations currently remain scarce at the global scale. A satellite remote sensing system could make a substantial contribution to tackling this problem. Here, we make initial steps towards the potential design of such a remote sensing system by: (1) identifying the properties of marine plastic debris amenable to remote sensing methods and (2) highlighting the oceanic processes relevant to scientific questions about marine plastic debris. Remote sensing approaches are reviewed and matched to the optical properties of marine plastic debris and the relevant spatio-temporal scales of observation to identify challenges and opportunities in the field. Finally, steps needed to develop marine plastic debris detection by remote sensing platforms are proposed in terms of fundamental science as well as linkages to ongoing planning for satellite systems with similar observation requirements.
Following the widespread assumption that a majority of ubiquitous marine microplastic particles originate from land-based sources, recent studies identify rivers as important pathways for microplastic particles (MPP) to the oceans. Yet a detailed understanding of the underlying processes and dominant sources is difficult to obtain with the existing accurate but extremely time-consuming methods available for the identification of MPP. Thus in the presented study, a novel approach applying short-wave infrared imaging spectroscopy for the quick and semi-automated identification of MPP is applied in combination with a multitemporal survey concept. Volume-reduced surface water samples were taken from transects at ten points along a major watercourse running through the South of Berlin, Germany, on six dates. After laboratory treatment, the samples were filtered onto glass fiber filters, scanned with an imaging spectrometer and analyzed by image processing. The presented method allows to count MPP, classify the plastic types and determine particle sizes. At the present stage of development particles larger than 450 μm in diameter can be identified and a visual validation showed that the results are reliable after a subsequent visual final check of certain typical error types. Therefore, the method has the potential to accelerate microplastic identification by complementing FTIR and Raman microspectroscopy. Technical advancements (e.g. new lens) will allow lower detection limits and a higher grade of automatization in the near future. The resulting microplastic concentrations in the water samples are discussed in a spatio-temporal context with respect to the influence (i) of urban areas, (ii) of effluents of three major Berlin wastewater treatment plants discharging into the canal and (iii) of precipitation events. Microplastic concentrations were higher downstream of the urban area and after precipitation. An increase in microplastic concentrations was discernible for the wastewater treatment plant located furthest upstream though not for the other two.
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