Detecting Internal Waves From Satellite Ocean Color Imagery

2006 ◽  
Author(s):  
Chung-Ru Ho ◽  
Feng-Chun Su ◽  
Nan-Jung Kuo ◽  
Shih-Jen Huang ◽  
Chun-Te Chen ◽  
...  
Keyword(s):  
Internal Wave ◽  
Internal Waves ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Chlorophyll Concentration ◽  
Theoretic Model ◽  
Near Surface ◽  
Sulu Sea ◽  
High Resolution Satellite Images ◽  
Wave Phenomena

Internal waves have been observed by lots of high resolution satellite images, such as Synthetic Aperture Radar (SAR) and optical images of SPOT and Landsat. These images are usually expensive. In this study, some free but lower spatial resolution satellite images are applied to observe the internal wave phenomena. The internal waves in the Sulu Sea are detected from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) onboard the Orbview-2 satellite. The SeaWiFS image has a spatial resolution of 1.1 km. It is acceptable to observe the internal wave phenomena while the soliton width is larger than the image resolution. The results show that the internal solitary in the Sulu Sea can be observed successfully with SeaWiFS chlorophyll images. The internal waves in the Sulu Sea have amplitudes of 10 to 90 m and wavelengths of 5 to 16 km. The large-amplitude internal solitary waves may significantly influence the near-surface chlorophyll concentration. The chlorophyll concentration would be lower when the depression internal waves passed through. A theoretic model is proposed and tested to estimate the amplitudes of internal waves from chlorophyll concentration images.

scholarly journals An Approach for the Pan Sharpening of Very High Resolution Satellite Images Using a CIELab Color Based Component Substitution Algorithm

2019 ◽  
Vol 9 (23) ◽  
pp. 5234 ◽  
Author(s):  
Rahimzadeganasl ◽  
Alganci ◽  
Goksel
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
Spatial Information ◽  
High Spatial Resolution ◽  
Critical Role ◽  
Study Region ◽  
High Resolution Satellite Images ◽  
Very High ◽  
Component Substitution ◽  
Cielab Color

Recent very high spatial resolution (VHR) remote sensing satellites provide high spatial resolution panchromatic (Pan) images in addition to multispectral (MS) images. The pan sharpening process has a critical role in image processing tasks and geospatial information extraction from satellite images. In this research, CIELab color based component substitution Pan sharpening algorithm was proposed for Pan sharpening of the Pleiades VHR images. The proposed method was compared with the state-of-the-art Pan sharpening methods, such as IHS, EHLERS, NNDiffuse and GIHS. The selected study region included ten test sites, each of them representing complex landscapes with various land categories, to evaluate the performance of Pan sharpening methods in varying land surface characteristics. The spatial and spectral performance of the Pan sharpening methods were evaluated by eleven accuracy metrics and visual interpretation. The results of the evaluation indicated that proposed CIELab color-based method reached promising results and improved the spectral and spatial information preservation.

Upslope Internal-Wave Stokes Drift, and Compensating Downslope Eulerian Mean Currents, Observed above a Lakebed

2016 ◽  
Vol 46 (6) ◽  
pp. 1947-1961 ◽  
Author(s):  
Stephen M. Henderson
Keyword(s):  
Boundary Layer ◽  
Internal Wave ◽  
Internal Waves ◽  
Outer Boundary ◽  
Stokes Drift ◽  
Surface Velocity ◽  
Intermediate Water ◽  
Mean Velocity ◽  
Near Surface ◽  
Velocity Fluctuations

AbstractIn a small lake, where flows were dominated by internal waves with 10–32-h period, slow but persistent mean transport of water over many wave periods was examined. Acoustic Doppler profilers (ADPs) and a vertical string of temperature loggers were deployed where the lower thermocline intersected the sloping lakebed. Near (<1 m above) the bed, internal waves, coherent with a lakewide seiche, propagated upslope at ~0.023 m s−1. Near-bed wave-induced water velocity fluctuations had a standard deviation of <0.02 m s−1. Near the surface, velocity fluctuations had similar magnitude, but lateral wave propagation was unclear. Averaged over many wave periods, the near-bed Eulerian velocity flowed downslope at ~0.01 m s−1, and was roughly cancelled by an upslope internal-wave Stokes drift (estimated by assuming that weakly nonlinear waves propagated without change of form). To examine net transport, while relaxing approximations used to estimate the Stokes drift, the observed temperature range (9°–25°C) was divided into 0.5°C increments, and the depth-integrated, wave-averaged flux of water in each temperature class was calculated. The coldest (near-bed) water was slowly transported onshore, opposite the Eulerian mean velocity. Onshore flux of warm near-surface water was comparable to an Eulerian-mean flux, indicating minimal near-surface Stokes drift. Intermediate water, from the middle of the water column and the outer boundary layer, was transported offshore by an offshore Stokes drift. The downslope near-bed Eulerian mean velocity, together with intensification of mean stratification within 0.4 m of the bed, may enhance boundary layer mixing.

scholarly journals Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 230
Author(s):  
Sultan Daud Khan ◽  
Louai Alarabi ◽  
Saleh Basalamah
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Multiple Scales ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Hybrid Network ◽  
High Resolution Satellite Images

Land cover semantic segmentation in high-spatial resolution satellite images plays a vital role in efficient management of land resources, smart agriculture, yield estimation and urban planning. With the recent advancement in remote sensing technologies, such as satellites, drones, UAVs, and airborne vehicles, a large number of high-resolution satellite images are readily available. However, these high-resolution satellite images are complex due to increased spatial resolution and data disruption caused by different factors involved in the acquisition process. Due to these challenges, an efficient land-cover semantic segmentation model is difficult to design and develop. In this paper, we develop a hybrid deep learning model that combines the benefits of two deep models, i.e., DenseNet and U-Net. This is carried out to obtain a pixel-wise classification of land cover. The contraction path of U-Net is replaced with DenseNet to extract features of multiple scales, while long-range connections of U-Net concatenate encoder and decoder paths are used to preserve low-level features. We evaluate the proposed hybrid network on a challenging, publicly available benchmark dataset. From the experimental results, we demonstrate that the proposed hybrid network exhibits a state-of-the-art performance and beats other existing models by a considerable margin.

scholarly journals Internal Wave Reflection on Shelf Slopes with Depth-Varying Stratification

2013 ◽  
Vol 43 (2) ◽  
pp. 248-258 ◽  
Author(s):  
Rob A. Hall ◽  
John M. Huthnance ◽  
Richard G. Williams
Keyword(s):  
Internal Wave ◽  
Internal Waves ◽  
Energy Flux ◽  
Internal Tide ◽  
Shelf Break ◽  
Maximum Slope ◽  
Near Surface ◽  
Shelf Slope ◽  
Linear Slope ◽  
Horizontal Wavelength

Abstract Reflection of internal waves from sloping topography is simple to predict for uniform stratification and linear slope gradients. However, depth-varying stratification presents the complication that regions of the slope may be subcritical and other regions supercritical. Here, a numerical model is used to simulate a mode-1, M2 internal tide approaching a shelf slope with both uniform and depth-varying stratifications. The fractions of incident internal wave energy reflected back offshore and transmitted onto the shelf are diagnosed by calculating the energy flux at the base of slope (with and without topography) and at the shelf break. For the stratifications/topographies considered in this study, the fraction of energy reflected for a given slope criticality is similar for both uniform and depth-varying stratifications. This suggests the fraction reflected is dependent only on maximum slope criticality and independent of the depth of the pycnocline. The majority of the reflected energy flux is in mode 1, with only minor contributions from higher modes due to topographic scattering. The fraction of energy transmitted is dependent on the depth-structure of the stratification and cannot be predicted from maximum slope criticality. If near-surface stratification is weak, transmitted internal waves may not reach the shelf break because of decreased horizontal wavelength and group velocity.

scholarly journals Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects

2020 ◽  
Vol 12 (24) ◽  
pp. 4158
Author(s):  
Mengmeng Li ◽  
Alfred Stein
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Spatial Arrangement ◽  
Remote Sensing Images ◽  
Land Use Types ◽  
High Resolution Satellite Images

Spatial information regarding the arrangement of land cover objects plays an important role in distinguishing the land use types at land parcel or local neighborhood levels. This study investigates the use of graph convolutional networks (GCNs) in order to characterize spatial arrangement features for land use classification from high resolution remote sensing images, with particular interest in comparing land use classifications between different graph-based methods and between different remote sensing images. We examine three kinds of graph-based methods, i.e., feature engineering, graph kernels, and GCNs. Based upon the extracted arrangement features and features regarding the spatial composition of land cover objects, we formulated ten land use classifications. We tested those on two different remote sensing images, which were acquired from GaoFen-2 (with a spatial resolution of 0.8 m) and ZiYuan-3 (of 2.5 m) satellites in 2020 on Fuzhou City, China. Our results showed that land use classifications that are based on the arrangement features derived from GCNs achieved the highest classification accuracy than using graph kernels and handcrafted graph features for both images. We also found that the contribution to separating land use types by arrangement features varies between GaoFen-2 and ZiYuan-3 images, due to the difference in the spatial resolution. This study offers a set of approaches for effectively mapping land use types from (very) high resolution satellite images.

scholarly journals AUTOMATED PANSHARPENING INFORMATION TECHNOLOGY OF SATELLITE IMAGES

2021 ◽  
pp. 123-132
Author(s):  
V. V. Hnatushenko ◽  
V. Yu. Kashtan
Keyword(s):  
Image Processing ◽  
Information Technology ◽  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Image Resolution ◽  
Resolution Image ◽  
Ndvi Index ◽  
High Resolution Satellite Images

Context. Nowadays, information technologies are widely used in digital image processing. The task of joint processing of satellite image obtained by different space systems that have different spatial differences is important. The already known pansharpening methods to improve the quality of the resulting image, there are new scientific problems associated with increasing the requirements for high-resolution image processing and the development of automated technology for processing the satellite data for further thematic analysis. Most spatial resolution techniques result in artifacts. Our work explores the major remote sensing data fusion techniques at pixel level and reviews the concept, principals, limitations and advantages for each technique with the program implementation of research. Objective. The goal of the work is analyze the effectiveness of the traditional pan-sharpening methods like the Brovey, the wavelet-transform, the GIHS, the HCT and the combined pansharpening method for satellite images of high-spatial resolution. Method. In this paper we propose the information technology for pansharpening high spatial resolution images with automation of choosing the best method based on the analysis of quantitative and qualitative evolutions. The method involves the scaling multispectral image to the size of the panchromatic image; using histogram equalization to adjust the primary images by aligning the integral areas of the sections with different brightness; conversion of primary images after the spectral correction on traditional pansharpening methods; analyze the effectiveness of the results obtained for conducts their detailed comparative visual and quantitative evaluation. The technology allows determining the best method of pansharpening by analyzing quantitative metrics: the NDVI index, the RMSE and the ERGAS. The NDVI index for the methods Brovey and HPF indicate color distortion in comparison with the reference data. This is due to the fact that the Brovey and HPF methods are based on the fusion of three channel images and do not include the information contained in the near infrared range. The RMSE and the ERGAS show the superiority of the combined HSVHCT-Wavelet method over the conventional and state-of-art image resolution enhancement techniques of high resolution satellite images. This is achieved, in particular, by preliminary processing of primary images, data processing localized spectral bases, optimized performance information, and the information contained in the infrared image. Results. The software implementing proposed method is developed. The experiments to study the properties of the proposed algorithm are conducted. Experimental evaluation performed on eight-primary satellite images of high spatial resolution obtained WorldView-2 satellite. The experimental results show that a synthesized high spatial resolution image with high information content is achieved with the complex use of fusion methods, which makes it possible to increase the spatial resolution of the original multichannel image without color distortions. Conclusions. The experiments confirmed the effectiveness of the proposed automated information technology for pansharpening high-resolution satellite images with the development of a graphical interface to obtain a new synthesized image. In addition, the proposed technology will effectively carry out further recognition and real-time monitoring infrastructure.

scholarly journals Effects of Spatial Resolution on the Satellite Observation of Floating Macroalgae Blooms

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1761
Author(s):  
Xinhua Wang ◽  
Qianguo Xing ◽  
Deyu An ◽  
Ling Meng ◽  
Xiangyang Zheng ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Yellow Sea ◽  
Satellite Images ◽  
Satellite Observation ◽  
The Yellow Sea ◽  
Low Resolution ◽  
High Resolution Satellite Images ◽  
High Resolution Images ◽  
Low Resolution Images

Satellite images with different spatial resolutions are widely used in the observations of floating macroalgae booms in sea surface. In this study, semi-synchronous satellite images with different resolutions (10 m, 16 m, 30 m, 50 m, 100 m, 250 m and 500 m) acquired over the Yellow Sea, are used to quantitatively assess the effects of spatial resolution on the observation of floating macroalgae blooms of Ulva prolifera. Results indicate that the covering area of macroalgae-mixing pixels (MM-CA) detected from high resolution images is smaller than that from low resolution images; however, the area affected by macroalgae blooms (AA) is larger in high resolution images than in low resolution ones. The omission rates in the MM-CA and the AA increase with the decrease of spatial resolution. These results indicate that satellite remote sensing on the basis of low resolution images (especially, 100 m, 250 m, 500 m), would overestimate the covering area of macroalgae while omit the small patches in the affected zones. To reduce the impacts of overestimation and omission, high resolution satellite images are used to show the seasonal changes of macroalgae blooms in 2018 and 2019 in the Yellow Sea.

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