- 标题
- 摘要
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- 实验方案
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Achieving high-resolution thermal imagery in low-contrast lake surface waters by aerial remote sensing and image registration
摘要： A two-platform measurement system for realizing airborne thermography of the Lake Surface Water Temperature (LSWT) with ~0.8 m pixel resolution (sub-pixel satellite scale) is presented. It consists of a tethered Balloon Launched Imaging and Monitoring Platform (BLIMP) that records LSWT images and an autonomously operating catamaran (called ZiviCat) that measures in situ surface/near surface temperatures within the image area, thus permitting simultaneous ground-truthing of the BLIMP data. The BLIMP was equipped with an uncooled InfraRed (IR) camera. The ZiviCat was designed to measure along predefined trajectories on a lake. Since LSWT spatial variability in each image is expected to be low, a poor estimation of the common spatial and temporal noise of the IR camera (nonuniformity and shutter-based drift, respectively) leads to errors in the thermal maps obtained. Nonuniformity was corrected by applying a pixelwise two-point linear correction method based on laboratory experiments. A Probability Density Function (PDF) matching in regions of overlap between sequential images was used for the drift correction. A feature matching-based algorithm, combining blob and region detectors, was implemented to create composite thermal images, and a mean value of the overlapped images at each location was considered as a representative value of that pixel in the final map. The results indicate that a high overlapping field of view (~95%) is essential for image fusion and noise reduction over such low-contrast scenes. The in situ temperatures measured by the ZiviCat were then used for the radiometric calibration. This resulted in the generation of LSWT maps at sub-pixel satellite scale resolution that revealed spatial LSWT variability, organized in narrow streaks hundreds of meters long and coherent patches of different size, with unprecedented detail.
关键词： Lake surface water temperature,Uncooled infrared camera,Image registration,Lake Geneva,Thermal imagery,Aerial remote sensing
更新于2025-09-23 15:23:52
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[IEEE 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Xiamen, China (2019.12.17-2019.12.20)] 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Ultra-wideband Absorber Based on Graphene Metamaterial
摘要： Surface water is a critical resource in semiarid West-African regions that are frequently exposed to droughts. Natural and artificial wetlands are of high importance for different livelihoods, particularly during the dry season, from October/November until May. However, wetlands largely go unmonitored. In this work, remote sensing is used to monitor wetlands in semiarid Burkina Faso over large areal extents along a gradient of different rainfall and land use characteristics. Time series of data from the Moderate Resolution Imaging Spectrometer (MODIS) from 2000 to 2012 is used for near-infrared (NIR)-based water monitoring using a latitudinal threshold gradient approach. The occurrence of 21 new water bodies with a size larger than 0.5 km2 over the 13-year analysis period results from a postclassification change detection. Yearly cumulative spatiotemporal analysis shows lower water extents in the drought seasons of 2000–2001, 2004–2005, and 2011–2012. Multiple wetlands indicate a positive trend toward a larger yearly maximum area, but a negative trend toward shorter flooding duration. Such a negative trend is observed particularly for natural wetlands. The temporal behavior of five selected case studies demonstrates that monthly negative anomalies of water-covered areas coincide with the occurrence of drought seasons. The successful application of remote sensing time series as a tool to monitor wetlands in semiarid regions is presented, and the potential of novel early warning indicators of drought from remote sensing is demonstrated.
关键词： Sahel,Moderate Resolution Imaging Spectrometer (MODIS),monitoring,drought indicators,Burkina Faso,wetlands,time series,surface water
更新于2025-09-19 17:13:59
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Ratiometric fluorometric determination of silver(I) by using blue-emitting silicon- and nitrogen-doped carbon quantum dots and red-emitting N-acetyl-L-cysteine-capped CdTe quantum dots
摘要： A ratiometric fluorometric assay for silver(I) is described. The method makes use of a dually emitting quantum dot hybrid, which is composed of (a) blue-fluorescent silicon- and nitrogen-doped carbon quantum dots (CQDs), and (b) of red-emitting CdTe quantum dots (QDs) capped with N-acetyl-L-cysteine. The red-emitting CdTe QDs undergo strong and specific quenching by Ag(I), whereas the blue-emitting N,Si-CQDs are not quenched. The two kinds of QDs are mixed and used as a ratiometric fluorescent probe. A linear relationship is found between the log of intensities [(I608/I441)0/(I608/I441)] and the concentration of Ag(I) in the range from 5.0–1000 nM, and the limit of detection (at S/N = 3) is 1.7 nM. Possible interferents (including 17 general metal ions, 12 anions and fulvic acid) do not interfere with the determination. The assay was successfully used for the determination of Ag(I) in surface water and wastewater samples. The fluorescence quenching mechanism of the ratiometric assay system was also discussed in detailed.
关键词： Fluorescence quenching mechanism,Surface water,Nitrogen-doped carbon dots,Silver ions,Silicon-doped carbon dots,Wastewaters,3-Aminopropyltriethoxysilane functionalized carbon dots,Quenching efficiency,Dual-emission quantum dots hybrid,Fluorescent probe
更新于2025-09-19 17:13:59
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Investigation on Perceptron Learning for Water Region Estimation Using Large-Scale Multispectral Images
摘要： Land cover classification and investigation of temporal changes are considered to be common applications of remote sensing. Water/non-water region estimation is one of the most fundamental classification tasks, analyzing the occurrence of water on the Earth’s surface. However, common remote sensing practices such as thresholding, spectral analysis, and statistical approaches are not sufficient to produce a globally adaptable water classification. The aim of this study is to develop a formula with automatically derived tuning parameters using perceptron neural networks for water/non-water region estimation, which we call the Perceptron-Derived Water Formula (PDWF), using Landsat-8 images. Water/non-water region estimates derived from PDWF were compared with three different approaches—Modified Normalized Difference Water Index (MNDWI), Automatic Water Extraction Index (AWEI), and Deep Convolutional Neural Network—using various case studies. Our proposed method outperforms all three approaches, showing a significant improvement in water/non-water region estimation. PDWF performance is consistently better even in cases of challenging conditions such as low reflectance due to hill shadows, building-shadows, and dark soils. Moreover, our study implemented a sunglint correction to adapt water/non-water region estimation over sunglint-affected pixels.
关键词： surface water bodies,Landsat-8,MNDWI,deep neural network,perceptron neural network,AWEI,PDWF
更新于2025-09-04 15:30:14