Application of satellite infrared data for mapping of thermal plume contamination in coastal ecosystem of Korea

The 5900 MW Younggwang nuclear power station on the west coast of Korea discharges warm water affecting coastal ecology [KORDI report (2003). Wide area observation of the impact of the operation of Younggwang nuclear power plant 5 and 6, No. BSPI 319-00-1426-3, KORDI, Seoul, Korea]. Here the spatial and temporal characteristics of the thermal plume signature of warm water are reported from a time series (1985-2003) of space-borne, thermal infrared data from Landsat and National Oceanic and Atmospheric Administration (NOAA) satellites. Sea surface temperature (SST) were characterized using advanced very high resolution radiometer data from the NOAA satellites. These data demonstrated the general pattern and extension of the thermal plume signature in the Younggwang coastal areas. In contrast, the analysis of SST from thematic mapper data using the Landsat-5 and 7 satellites provided enhanced information about the plume shape, dimension and direction of dispersion in these waters. The thermal plume signature was detected from 70 to 100 km to the south of the discharge during the summer monsoon and 50 to 70 km to the northwest during the winter monsoon. The mean detected plume temperature was 28 degrees C in summer and 12 degrees C in winter. The DeltaT varied from 2 to 4 degrees C in winter and 2 degrees C in summer. These values are lower than the re-circulating water temperature (6-9 degrees C). In addition the temperature difference between tidal flats and offshore (SSTtidal flats - SSToffsore) was found to vary from 5.4 to 8.5 degrees C during the flood tides and 3.5 degrees C during the ebb tide. The data also suggest that water heated by direct solar radiation on the tidal flats during the flood tides might have been transported offshore during the ebb tide. Based on these results we suggest that there is an urgent need to protect the health of Younggwang coastal marine ecosystem from the severe thermal impact by the large quantity of warm water discharged from the Younggwang nuclear power plant.     

Causes of the Regional Variability in Observed Sea Level,Sea Surface Temperature and Ocean Colour Over the Period 1993–2011

We analyse the regional variability in observed sea surface height (SSH), sea surface temperature (SST) and ocean colour (OC) from the ESA Climate Change Initiative datasets over the period 1993–2011. The analysis focuses on the signature of the ocean large-scale climate fluctuations driven by the atmospheric forcing and do not address the mesoscale variability. We use the ECCO version 4 ocean reanalysis to unravel the role of ocean transport and surface buoyancy fluxes in the observed SSH, SST and OC variability. We show that the SSH regional variability is dominated by the steric effect (except at high latitude) and is mainly shaped by ocean heat transport divergences with some contributions from the surface heat fluxes forcing that can be significant regionally (confirming earlier results). This is in contrast with the SST regional variability, which is the result of the compensation of surface heat fluxes by ocean heat transport in the mixed layer and arises from small departures around this background balance. Bringing together the results of SSH and SST analyses, we show that SSH and SST bear some common variability. This is because both SSH and SST variability show significant contributions from the surface heat fluxes forcing. It is evidenced by the high correlation between SST and buoyancy-forced SSH almost everywhere in the ocean except at high latitude. OC, which is determined by phytoplankton biomass, is governed by the availability of light and nutrients that essentially depend on climate fluctuations. For this reason, OC shows significant correlation with SST and SSH. We show that the correlation with SST displays the same pattern as the correlation with SSH with a negative correlation in the tropics and subtropics and a positive correlation at high latitude. We discuss the reasons for this pattern.     

Spatial Patterns of Sea Level Variability Associated with Natural Internal Climate Modes

Sea level rise (SLR) can exert significant stress on highly populated coastal societies and low-lying island countries around the world. Because of this, there is huge societal demand for improved decadal predictions and future projections of SLR, particularly on a local scale along coastlines. Regionally, sea level variations can deviate considerably from the global mean due to various geophysical processes. These include changes of ocean circulations, which partially can be attributed to natural, internal modes of variability in the complex Earth’s climate system. Anthropogenic influence may also contribute to regional sea level variations. Separating the effects of natural climate modes and anthropogenic forcing, however, remains a challenge and requires identification of the imprint of specific climate modes in observed sea level change patterns. In this paper, we review our current state of knowledge about spatial patterns of sea level variability associated with natural climate modes on interannual-to-multidecadal timescales, with particular focus on decadal-to-multidecadal variability. Relevant climate modes and our current state of understanding their associated sea level patterns and driving mechanisms are elaborated separately for the Pacific, the Indian, the Atlantic, and the Arctic and Southern Oceans. We also discuss the issues, challenges and future outlooks for understanding the regional sea level patterns associated with climate modes. Effects of these internal modes have to be taken into account in order to achieve more reliable near-term predictions and future projections of regional SLR.     

地震岩问题

在过去的82年(1931—2013)中,共出现了39个成因术语。在这39个术语中,只有10个是真正沉积成因的(例如浊积岩),其他29个术语都是比较草率地提出的(just jargons;例如地震岩、海啸岩等)。“地震岩”(seismites)这一成因术语是赛拉赫(Seilacher,1969)提出的,它指沉积记录中的古地震。这是一个误用的(misnomer)术语。赛拉赫仅仅根据美国加利福尼亚州的中新统蒙特雷组(Miocene Montery Formation)10,m厚的露头观察,就匆忙地提出这个术语。他并未对这个露头进行认真的科学研究。最根本的问题是,地震是一个触动因素^①,并不是一个沉积作用。在古代的沉积记录中,鉴别不出各种触动因素(trigger)^②,因为自然界不能保留触动因素曾经存在过的证据。“软沉积物变形构造”(soft-sediment deformation structures,可简称为SSDS),通常被人们用来作为地震岩的鉴别标志,其实它是“液化作用”(liquefacation)的结果。液化作用可以由21种触动因素中的任何一种引起,其中包括地震(earthquake)、陨石冲击(meteorite impact)、海啸(tsunami)、沉积负载(sediment loading)等。在死海盆地中的角砾碎屑,所谓典型的由地震引起的沉积,实际上是常见的同沉积作用的碎屑流的产物,与地震没有关系。其他类型的SSDS,例如 “双向叠层构造”(duplex-like structures)和碎屑注入体(clastic injections),也可以说是同沉积作用的产物,与地震没有关系。世界上广泛分布的砂岩储油层,包括墨西哥湾、北海、挪威海、尼日利亚、赤道几内亚、加逢、孟加拉湾,都表明这些地区的SSDS的第一起因(primary cause)是沉积负载,其证据是可以观察到的和令人信服的。海啸及陨石冲击,都可以形成大量的沉积,例如SSDS的侧向延伸(lateral extent of SSDS),但这些沉积都不能当作地震的鉴别标志。位于印度东部大陆边缘的Krishna-Godavari(KG)盆地中的SSDS,是多种触动因素^①引起的沉积物的失控和液化作用的结果,即多成因的SSDS。由于这些原因,“地震岩”这个成因术语确实没有什么科学价值,应当废弃。     

Interannual Sea Level Variations in the South China Sea Over 1950–2009

Spatial patterns of interannual sea level variations in the South China Sea (SCS) are investigated by analyzing an EOF-based 2-dimensional past sea level reconstruction from 1950 to 2009 and satellite altimetry data from 1993 to 2009. Long-term tide gauge records from 14 selected stations in this region are also used to assess the quality of reconstructed sea levels and determine the rate of sea level along the coastal area. We found that the rising rate of sea levels derived from merged satellite altimetry data during 1993–2009 and past sea level reconstruction over 1950–2009 is about 3.9 ± 0.6 mm/yr and 1.7 ± 0.1 mm/yr, respectively. For the longer period, this rate is not significantly different from the global mean rate (of 1.8 ± 0.3 mm/yr). The interannual mean sea level of the SCS region appears highly correlated with Niño 4 indices (a proxy of El Niño-Southern Oscillation/ENSO), suggesting that the interannual sea level variations over the SCS region is driven by ENSO events. Interpolation of the reconstructed sea level data for 1950–2009 at sites where tide gauge records are of poor quality (either short or gapped) show that sea level along the Chinese coastal area is rising faster than the global mean rate of 1.8 mm/yr. At some sites, the rate is up to 2.5 mm/yr.     

Hyperspectral Radiometry to Quantify the Grades of Iron Ores of Noamundi and Joda Mines,Eastern India

This paper present the results of a preliminary study to assess the potential of the visible, NIR and SWIR energy of the EMR in differentiating iron ores of different grades in a rapid manner using hyperspectral radiometry. Using different iron ore samples from Noamundi and Joda mines, Jharkhand and Orissa, states of India, certain spectro-radiometric measurements and geochemical analysis were carried out and the results have been presented. It was observed that the primary spectral characteristics of these iron ores lie in the 850 to 900 nm and 650–750 nm regions. The spectral parameters for each curve used for studying the iron ores are: (i) the slopes of the spectral curve in 685–725 nm region; (ii) position of the peak with respect to wavelength in 730–750 nm region and (iii) radius of curvature of the absorption trough in the 850–900 nm region. Comparison of these spectral parameters and the geochemistry of the samples indicates that the position of the peak of the curve in 730–750 nm region shifts towards longer wavelength with increasing iron oxide content, while the slope of the curvature in the 685–725 nm region has a strong negative correlation with the iron oxide content of the samples. Similarly, a strong negative correlation is observed between the radius of curvature of the 850–900 nm absorption trough and the iron oxide content. Such strong correlations indicate that hyperspectral radiometry in the visible and NIR regions can give a better estimate and quantification of the grades of iron ores. This study has demonstrated that generation of empirical models using hyperspectral radiometric techniques is helpful to quantify the grade of iron ores with limited geochemical analysis.     

Modeling the underwater light field fluctuations in coastal oceanic waters: Validation with experimental data

Modeling of the wave-induced underwater light fluctuations at near-surface depths in coastal oceanic waters is challenging because of the surface roughness and strong anisotropic effects of the light field. In the present work, a simple and computationally efficient radiative transfer model is used for the wind-driven sea surface for simulating underwater light fields such as downwelling irradiance (E_d), upwelling irradiance (E_u), and upwelling radiance (L_u) in a spatial domain. It is an extension of our previous work that essentially combines the air–sea interface of the wind-driven sea surface with transmittance and reflectance along with the diffuse and direct components of the homogenous and inhomogeneous water column. The present model simulates underwater light fields for any possible values of absorption and backscattering coefficients. To assess the performance of the model, the E_d, E_u, and L_u profiles predicted by the model are compared with experimental data from relatively clear and turbid coastal waters. Statistical results show significantly low mean relative differences regardless of the wavelength. Comparison of the simulated and in-situ time series data measured over rough sea surfaces demonstrates that model-observation agreement is good for the present model. The Hydrolight model when implemented with the modified bottom reflectance and phase function provides significantly better results than the original Hydrolight model without consideration of the bottom slope and vertically varying phase function. However, these results are non-spatial and have errors fluctuating at different wavelengths. To further demonstrate the efficiency of the present model, spatial distribution patterns of the underwater light fields are simulated based on the measured data from a coastal station for different solar zenith angles (under sunny condition). Simulated wave-induced fluctuations of the underwater lights fields show a good consistency with in-situ data for a few near-surface depths. The present model also provides a reasonable approximation for simulating wave-induced effects on the downward irradiance field and its anisotropic conditions caused by the surface roughness, wavelength and angle of incidence.     

SAR polarimetric decomposition with ALOS PALSAR-1 for agricultural land and other land use/cover classification: case study in Rajasthan,India

Synthetic aperture radar (SAR) has operational applications in crop mapping and monitoring in many countries due to the distinct backscatter signature at various stages of crop growth. Polarimetric analysis of SAR data from different satellites was used for information extraction from different types of scatters in imaged terrain. The scattering processes were analyzed through the received scatter matrix derived from the target decomposition of SAR data. Three decomposition techniques, namely Freeman–Durden, Cloude–Pottier and Touzi decomposition of the ALOS PALSAR-1 data, were used in this study to extract land use/cover information with a specific emphasis on agriculture. The decomposed output parameters from these techniques were classified with supervised classifier of support vector machine (SVM) using region of interest (ROI) selected land use/cover classes. An accuracy assessment for the classified output was carried out using the ROI. The Ramgarh village in Jaisalmer district of Rajasthan with the predominance of agricultural land, sand dunes and settlements was chosen as the study area. Freeman–Durden decomposition resulted in the highest overall accuracy of about 85% in the land use/cover classification among the three decomposition techniques adopted in the study. It was also observed that the accuracy of land use/cover mapping derived from Cloude–Pottier and Touzi decompositions improved with the use of eigenvalues in the SVM classification. Higher accuracies in the classification of agriculture land were noted with all the three decomposition techniques. The four parameters of Cloude–Pottier (H, A, α, β) and Touzi (α _s, Φ _s, ψ, τ) decompositions improved the classification accuracy for all the classes due to eigenvalues. The overall classification accuracy was above 88% for both the decomposition techniques with four parameters. The soil moisture values for agriculture land and sand dunes were validated through soil moisture maps generated using Oh 1992 and 2004 models.