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The preparation of high‐performance rubber‐plastic blending elastomers has always been a research hotspot in the field of petroleum equipment. The two‐phase separation structure of the blend determines the macroscopic properties of the materials. It is of great significance to deeply explore the influence of processing technology on the two‐phase separation structure. In this paper, polyamide 6 (PA6)/nitrile rubber blends were prepared via the melt–compounding method. To meet the requirements of oil production and oil transport equipment, we investigated systematically the mechanical properties of the specimens exposed to different environments. The oil resistance and high‐temperature resistance of the blends were enhanced considerably with the addition of PA6. The temperature and pressure fields induced a novel transition from particle into fibrous phase based on Ostwald's ripening. However, this brand new transformation showed no benefits to the improvement of the frictional performance. Meanwhile, the relationship between aggregate structure and mechanical performance was demonstrated from the aspects of crystallization behavior and phase morphology. [ABSTRACT FROM AUTHOR]

Highlights For traditional materials, a polymer composite with high performance and large‐scale production is still the goal pursued by researchers. In our work, polyamide 6/calcium sulfate whiskers (PA6/SCW) composites were fabricated via melt‐compouding method. The calcium sulfate whisker based on gypsum mineral was used as reinforcement. After grafting silane coupling agent on the whisker surface, the whiskers showed a significant reinforcing effect in polyamide. The mechanics and tribology performance of the samples had been significantly inhanced. Based on the nucleation mechanism of lattice matching, calcium sulfate whiskers have obvious heterogeneous nucleation effect in PA6 matrix, while the crystallization period was slightly prolonged. This was caused by the network structure formed by the whiskers in the matrix, which impeded the free movement of polymer chain segments. In combination with the orientation degree of the molecular chains measured by the interdigital electrode, the reinforcing effect of the oriented PA6 specimens was derived from the orientation arrangement of the whiskers and the efficient load transfer. Mechanical and tribological properties of composite were significantly improved. The composite could achieved large‐scale production due to simple preparation. The whiskers had obvious heterogeneous nucleation effect in matrix. The reinforcing effect was derived from efficient load transfer from whiskers. [ABSTRACT FROM AUTHOR]

Atmospheric correction is one of the major challenges in ocean color remote sensing, thus threatening comprehensive evaluation of water quality within aquatic environments. In this study, five state-of-the-art atmospheric correction (AC) processors (i.e. Acolite, C2RCC, iCOR, L2gen, and Polymer) were applied to Operational Land Imager (OLI) Landsat-8 scenes and evaluated against in situ measurements across various types of waters worldwide. A total of 262 matchups between in situ measured and satellite-derived remote sensing reflectance (Rrs) at 20 sites were obtained between August 2013 and August 2021. Classification of optical water types (OWTs) was carried out using in situ measurements with matched satellite observations. OWT-specific analysis demonstrated that L2gen produced the most accurate Rrs with R2 ≥ 0.74 and root mean squared error (RMSE) ≤ 0.0018 sr–1 for the four visible bands of OLI, followed by Polymer, C2RCC, iCOR, and Acolite. In terms of Rrs spectral similarity, C2RCC yielded the lowest spectral angle (SA) of 8.55°, followed by L2gen (SA = 9.20°). The advantage and disadvantage of each AC scheme were discussed. Recommendations to improve the accuracy for atmospheric correction were made, such as polarization observations and concurrent aerosol and ocean color measurements. [ABSTRACT FROM AUTHOR]

Accurate passive 3D face reconstruction is of great importance with various potential applications. Three-dimensional polarization face reconstruction is a promising approach, but one bothered by serious deformations caused by an ambiguous surface normal. In this study, we propose a learning-based method for passive 3D polarization face reconstruction. It first calculates the surface normal of each microfacet at a pixel level based on the polarization of diffusely reflected light on the face, where no auxiliary equipment, including artificial illumination, is required. Then, the CNN-based 3DMM (convolutional neural network; 3D morphable model) generates a rough depth map of the face with the directly captured polarization image. The map works as an extra constraint to correct the ambiguous surface normal obtained from polarization. An accurate surface normal finally allows for an accurate 3D face reconstruction. Experiments in both indoor and outdoor conditions demonstrate that accurate 3D faces can be well-reconstructed. Moreover, with no auxiliary equipment required, the method ensures a total passive 3D face reconstruction. [ABSTRACT FROM AUTHOR]

Pumice rafts are considered to be a long-range drifting agent that promotes material exchange and the dispersal of marine species. Large ones can also interfere with vessel navigation and have a negative impact on the social economy and marine ecosystems. Synoptic observations from the Multispectral Instrument (MSI) on-board Sentinel-2, with a spatial resolution of up to 10 m, provide an excellent means to monitor and track pumice rafts. In this study, the use of a Spectral-Feature-Based Extraction (SFBE) algorithm to automatically discriminate and extract pumice on the ocean surface from submarine volcano eruptions was proposed. Specifically, a Pumice Raft Index (PRI) was developed based on the spectral signatures of pumice in MSI imagery to identify potential pumice features. After pre-processing, the PRI image was then subjected to a series of per-pixel and object-based processes to rule out false-positive detections, including shallow water, striped edges, mudflats, and cloud edges. The SFBE algorithm showed excellent performance in extracting pumice rafts and was successfully applied to extract pumice rafts near the Fiji Yasawa islands in 2019 and Hunga Tonga island in 2022, with an overall pumice extraction accuracy of 95.5% and a proportion of pixels mis-extracted as pumice of <3%. The robustness of the algorithm has also been tested and proved through applying it to data and comparing its output to results from previous studies. The timely and accurate detection of pumice using the algorithm proposed here is expected to provide important information to aid in response actions and ecological assessments, and will lead to a better understanding of the fate of pumice. [ABSTRACT FROM AUTHOR]

Large volumes of crude oil accidentally released into the sea may cause irreversible adverse impacts on marine and coastal environments. Large swath optical imagery, acquired using platforms such as the moderate-resolution imaging spectroradiometer (MODIS), is frequently used for massive oil spill detection, attributing to its large coverage and short global revisit, providing rich data for oil spill monitoring. The aim of this study was to develop a suitable approach for massive oil spill detection in sun glint optical imagery. Specifically, preprocessing procedures were conducted to mitigate the inhomogeneous light field over the spilled area caused by sun glint, enhance the target boundary contrast, and maintain the internal homogeneity within the target. The image was then segmented into super-pixels based on a simple linear clustering method with similar characteristics of color, brightness, and texture. The neighborhood super-pixels were merged into target objects through the region adjacency graph method based on the Euclidean distance of their colors with an adaptive termination threshold. Oil slicks from the generated bright/dark objects were discriminated through a decision tree with parameters based on spectral and spatial characteristics. The proposed approach was applied to oil spill detection in MODIS images acquired during the Montara oil spill in 2009, with an overall extraction precision of 0.8, recall of 0.838, and F1-score of 0.818. Such an approach is expected to provide timely and accurate oil spill detection for disaster emergency response and ecological impact assessment. [ABSTRACT FROM AUTHOR]

Objective: The objective was to analyze the clinical characteristics and pathological characteristics of sural biopsy in nitrous oxide (N2O) -induced peripheral neuropathy. Methods: We recruited 18 patients with N2O abuse-induced neurological disorders and reported their demographic data, clinical manifestations, laboratory examinations, and nerve conduction studies. Seven patients underwent sural nerve biopsy pathologic examination. Results: All 18 patients had polyneuropathy, the nerve conduction results showed significant reductions in motor and sensory amplitudes, slowing of conduction velocities, and prolongation of latencies in most tested nerves compared to the controls. Toluidine blue staining of semi-thin sections of sural nerve biopsy showed decreased myelinated nerve fiber density, increased thin myelinated nerve fiber density, and axonal regeneration. Electron microscopy showed axonal degeneration and nerve regeneration. Conclusion: The main manifestations of peripheral nerve damage caused by the abuse of N2O are lower limb weakness and distal sensory disorder. The nerve conduction study results demonstrated that mixed axonal and demyelinating neuropathy was the most common type of neuropathy. Sural biopsy showed the main pathological change was chronic axonal degeneration. [ABSTRACT FROM AUTHOR]

Petroleum extraction, transportation, and consumption in the marine environment contribute to a large portion of anthropogenic oil spills into the ocean. While previous research focuses more on large oil spill accidents from oil tankers or offshore oil platforms, there are few systematic records on occasional regional oil spills. In this study, optical imagery from Landsat-8 OLI was used to detect oil slicks on the ocean surface through spatial analysis and spectral diagnosis in the northern South China Sea (NSCS). The source of the slicks was identified through datasets from traffic density and platform locations. A total of 632 oil slicks were detected in the NSCS from 2015 to 2019, where 57 were from platforms sources, and 490 were from ships. The average area of the detected slicks was 4.8 km2, and half of the slicks had areas <1.7 km2. Major oil spill hot spots included coastal Guangdong (ship origins), southeast and northeast Dongsha Island (ship origins), middle of south Beibu Gulf (ship and platform origins), and southeast Pearl River Estuary (platform origins). Through this study, we demonstrate the capability of medium-resolution optical imagery in monitoring regional oil spills. Such results and methods may help in near real-time oil spill monitoring and further environmental assessments. [ABSTRACT FROM AUTHOR]

Total suspended solid (TSS) and chlorophyll‐a (Chl‐a) are critical indicators of water quality. Moderate‐resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite provides a critical tool to map TSS and Chl‐a on a daily basis. However, robust algorithms are required to retrieve TSS and Chl‐a from MODIS/Aqua images in the turbid Pearl River Estuary (PRE). Here, a new artificial neural network algorithm was developed to estimate TSS and Chl‐a in the PRE using a large amount of in situ measurements spanning more than a decade. Cross‐validation showed that the ANN‐based algorithm presented good performance with R2s of 0.73 and 0.66 for TSS and Chl‐a, respectively. The ANN‐based algorithm outperformed existing empirical and semi‐analytical algorithms. By implementing the proposed algorithm to MODIS/Aqua images, TSS and Chl‐a from August 2002 to July 2020 over the PRE were obtained. The results show that TSS decreased from northwest to southeast in all seasons while peaked in winter and summer. Chl‐a peaked in summer and the high Chl‐a patch transferred from the upper reach to the lower reach of the PRE with the increase of river discharge. TSS decreased at an annual rate of ∼0.13 mg L−1 while Chl‐a increased at an annual rate of ∼0.05 mg m−3 in the last 18 years. Our findings suggest that machine learning provides a practical approach to estimate TSS and Chl‐a in turbid estuaries via MODIS/Aqua images and the approach developed in this study can be applied to other turbid waters with similar optical properties. Plain Language Summary: Using in situ measured data covering a wide range of values, a robust machine learning model was trained and validated to estimate total suspended solid (TSS) and chlorophyll‐a (Chl‐a) concentrations from satellite‐derived reflectance. The new model was then applied to daily MODIS/Aqua imagery over the Pearl River Estuary (PRE) from August 2002 to July 2020. Significant intra‐annual characteristics were observed for both TSS and Chl‐a, which were likely mainly related to river discharge and wind. In the context of rapid urbanization and climate change, TSS presented a significant decreasing trend at an annual rate of ∼0.13 mg L−1 (R2 = 0.59) while Chl‐a showed a significant increasing trend at an annual rate of ∼0.05 mg m−3 (R2 = 0.37) in the last 18 years. The results of this study issued an alert that eutrophication in the PRE is accelerating, and the new model can assist in the monitoring and management of water quality in the Pearl River estuary. Key Points: A machine learning‐based approach was developed to estimate total suspended solid (TSS) and chlorophyll‐a (Chl‐a) in the turbid Pearl River Estuary (PRE)Satellite‐derived time series of TSS and Chl‐a in the PRE presented significant spatiotemporal variationsTSS decreased at an annual rate of ∼0.13 mg L−1 while Chl‐a increased at an annual rate of ∼0.05 mg m−3 in the last 18 years [ABSTRACT FROM AUTHOR]

Monthly sea surface temperature (SST) and chlorophyll-a (Chl-a) concentration during 1997–2018 in the Northern South China Sea (NSCS) were gap-filled using the Data INterpolating Empirical Orthogonal Functions (DINEOFs) approach. Good accuracy of the reconstructed data was verified based on an independent cross-validation data set. Significant spatial heterogeneities and seasonal variations were observed in the cloud-free SST and Chl-a data. An apparent decreasing trend in SST was found along the coast of Guangdong, east of Hainan Island, and in the Taiwan Strait with an average decreasing rate amounting to 0.01 °C per year. Increase in SST was observed in Beibu Gulf and the deep offshore region in the NSCS at an average rate as high as 0.009 °C per year. An increasing trend in Chl-a was detected in the northern Beibu Gulf, near the mouth of the Pearl River estuary, and around Shantou with an average rate of 0.005 mg $\cdot \text{m}^{-3}$ per year. EOF analysis of SST and Chl-a time series showed that the first three modes of both SST and Chl-a can explain most of their variances with accumulative contributions of 99.1% and 82.3%, respectively. The effects of surface net heat flux (NHF), Oceanic Niño Index (ONI), wind speed, and river flux on the first three modes of SST and Chl-a were quantitatively elucidated. Factors that affected the temporal and spatial variations of Chl-a and SST in the NSCS were qualitatively discussed, including El Niño-Southern Oscillation (ENSO), monsoon, upwelling, typhoon, internal waves, and Kuroshio intrusion. [ABSTRACT FROM AUTHOR]

Dense Sialon fibers/barium aluminosilicate (BAS) matrix self‐reinforced composite was synthesized by one‐step method without applying pressure before or during sintering process. In this work, BAS matrix and internal Sialon fibers were synthesized successfully by directly sintering precursor powders at 1400°C for 4 h. The BAS is not only introduced as a structural matrix material, but also as an effective liquid phase sintering aid to attain full densification bulk sample. The results show that the diameter of fibers is about 100–200 nm, and the quantity of the fibers can be changed with different temperatures. The obtained Sialon fibers/BAS matrix composites exhibit better toughness due to fiber bridging, crack deflection, and pullout. The sample with 3.5 wt% carbon content has maximum value of flexural strength (74.5 ± 3.5 MPa) and fracture toughness (3.2 ± 0.20 MPa·m1/2). The growth of the Sialon fibers follows a vapor‐liquid‐solid (V‐L‐S) mechanism. These findings featured with a green and simple preparation process provide a facile strategy to design and fabricate the bulk ceramic matrix composites in complex shapes and different sizes. [ABSTRACT FROM AUTHOR]

Significant advances have been made in the development of self-healing synthetic polymer materials in recent years. This review article discusses the recent progress in preparation, characterization and properties of different kinds of intrinsic self-healing elastomers based on reversible covalent bonds and dynamic supramolecular chemistry. Healing conditions, mechanical property recovery and healing efficiency are the main discussion topics. Potential applications, challenges and future prospects in self-healing elastomer fields are also discussed in the last part of this review. [ABSTRACT FROM AUTHOR]

Optical remote sensing is one of the most commonly used techniques to detect oil in the surface ocean. This is because oil has optical properties that are different from water to modulate oil–water spatial and spectral contrasts. However, understanding these contrasts is challenging because of variable results from laboratory and field experiments as well as from different observing conditions and spatial/spectral resolutions of remote sensing imagery. Here, through reviewing published oil–water spectral contrasts and analyzing remotely sensed spectra collected by several satellite and airborne sensors (MERIS, MODIS, MISR, Landsat, and AVIRIS) from the Deepwater Horizon oil spill, we provide the interpretation of the spatial/spectral contrasts of various oil slicks and discuss the challenges in such interpretations. In addition to oil thickness, several other factors also affect oil–water spatial/spectral contrasts, including sun glint strength, oil emulsification state, optical properties of oil covered water, and spatial/spectral resolutions of remote sensing imagery. In the absence of high spatial- and spectral-resolution imagery, a multistep scheme may be used to classify oil type (emulsion and non-emulsion) and to estimate relative oil thickness for each type based on the known optical properties of oil, yet such a scheme requires further research to improve and validate. [ABSTRACT FROM AUTHOR]

Abstract An oil platform in the Mississippi Canyon 20 (MC-20) site was damaged by Hurricane Ivan in September 2004. In this study, we use medium- to high-resolution (10–30 m) optical remote sensing imagery to systematically assess oil spills near this site for the period between 2004 and 2016. Image analysis detects no surface oil in 2004, but ~40% of the cloud-free images in 2005 show oil slicks, and this number increases to ~70% in 2006–2011, and >80% since 2012. For all cloud-free images from 2005 through 2016 (including those without oil slicks), delineated oil slicks show an average oil coverage of 14.9 km2/image, with an estimated oil discharge rate of 48 to ~1700 barrels/day, and a cumulative oil-contaminated area of 1900 km2 around the MC-20 site. Additional analysis suggests that the detected oil slick distribution can be largely explained by surface currents, winds, and density fronts. Highlights • Over 80% of the cloud-free images show oil slicks near the MC-20 site in 2012–2016. • Average oil coverage area is 14.9 km2 for all cloud-free images in 2005–2016. • A cumulative oil-contaminated footprint of 1900 km2 around the site is detected. • Average oil discharge rate is estimated to range from 48 to ~1700 barrels/day. • River plume dynamics largely influences the variability in oil slick distributions. [ABSTRACT FROM AUTHOR]

Endowing thermoset polymer materials with self-healing and recyclable properties by incorporating dynamic bonds into the polymer networks is of vital realistic significance. However, such materials are still limited because of the difficult synthetic process and expensive dynamic monomers or environment-unfriendly catalysts. Herein, catalyst-free and colorless transparent healable and recyclable poly(oxime-urethane) elastomers based on dynamic oxime–carbamate bonds were developed with scaling-up capability using low-cost and commercially available compounds. These materials display remarkable comprehensive properties and relatively low relaxation activation energy (∼29.7 kcal mol−1) which results in excellent healable and recyclable performance. The poly(oxime-urethane) with a crosslink density ν of ∼0.2 mmol cm−3 possesses a tensile strength of ∼13.5 MPa, a breaking strain of ∼812%, toughness up to ∼40 MJ m−3, an excellent elastic recovery of ∼90% and an average optical transmittance of ∼86% in the visible range. This material can be completely healed at 110 °C in 0.5 h. In situ structural characterizations revealed that the dynamic properties originate from the reversibility of oxime–carbamate bonds and hydrogen bonds. [ABSTRACT FROM AUTHOR]

The critical angle is the angle at which the contrast of oil slicks reverse their contrasts against the surrounding oil-free seawater under sunglint. Accurate determination of the critical angle can help estimate surface roughness and refractive index of the oil slicks. Although it’s difficult to determine a certain critical angle, the potential critical angle range help to improve the estimation accuracy. In this study, the angle between the viewing direction and the direction of mirror reflection is used as an indicator for quantifying the critical angle and could be calculated from the solar/viewing geometry from observations of the Moderate Resolution Imaging Spectroradiometer (MODIS). The natural seep oil slicks in the Gulf of Mexico were first delineated using a customized segmentation approach to remove noise and apply a morphological filter. On the basis of the histograms of the brightness values of the delineated oil slicks, the potential range of the critical angle was determined, and then an optimal critical angle between oil slicks and seawater was then determined from statistical and regression analyses in this range. This critical angle corresponds to the best fitting between the modeled and observed surface roughness of seep oil slicks and seawater. [ABSTRACT FROM AUTHOR]

The distribution and variation of phytoplankton size class (PSC) are key to understanding ocean biogeochemical processes and ecosystems. Remote sensing of the PSC in the East China Sea (ECS) remains a challenge, although many algorithms have been developed to estimate PSC. Here based on a local dataset from the ECS, a regional model was tuned to estimate the PSC from the spectral features of normalized phytoplankton absorption (aph/ using a principal component analysis approach. Before applying the refined PSC model to MODIS (Moderate Resolution Imaging Spectroradiometer) data, reconstructing satellite remote sensing reflectance (Rrs/ at 412 and 443 nm was critical through modeling them from Rrs between 469 and 555 nm using multiple regression analysis. Satellite-derived PSC results compared well with those derived from pigment composition, which demonstrated the potential of satellite ocean color data to estimate PSC distributions in the ECS from space. Application of the refined PSC model to the reconstructed MODIS data from 2003 to 2016 yielded the seasonal distributions of the PSC in the ECS, suggesting that the PSC distributions were heterogeneous in both temporal and spatial scales. Micro-phytoplankton were dominant in coastal waters throughout the year, especially in the Changjiang estuary. For the middle shelf region, the seasonal shifts from the dominance of micro- and nano-phytoplankton in the winter and spring to the dominance of nano- and pico-phytoplankton in the summer and autumn were observed. Pico-phytoplankton were especially dominant in the Kuroshio region in the spring, summer, and autumn. The seasonal variations of the PSC in the ECS were probably affected by a combination of the water column stability, upwelling, sea surface temperature, and the Kuroshio. Additionally, human activity and riverine discharge might also influence the PSC distribution in the ECS, especially in the coastal region. [ABSTRACT FROM AUTHOR]

Abstract: Satellite remote sensing is well known to play a critical role in monitoring marine accidents such as oil spills, yet the recent SANCHI oil tanker collision event in January 2018 in the East China Sea indicates that traditional techniques using synthetic aperture radar or daytime optical imagery could not provide timely and adequate coverage. In this study, we show the unprecedented value of Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire product and Day/Night Band data in tracking the oil tanker's drifting pathway and locations when all other means are not as effective for the same purpose. Such pathway and locations can also be reproduced with a numerical model, with root‐mean‐square error of <15 km. While high‐resolution optical imagery after 4 days of the tanker's sinking reveals much larger oil spill area (>350 km2) than previous reports, the impact of the spilled condensate oil on the marine environment requires further research. [ABSTRACT FROM AUTHOR]

The critical angle at which the brightness of oil slicks and oil-free seawater is reversed occurs under sunglint and is often shown as an area of uncertainty due to different roughness and surface Fresnel reflection parameters. Consequently, differentiating oil slicks from the seawater in these areas using optical sensors is a challenge. Polarized optical remote sensing techniques provide complementary information for intensity imagery with different physical properties and, thus, possess the ability to resolve this difficult problem. In the polarized reflectance model, the degree of linear polarization (DOLP) of sunglint depends on accurately knowing the Stokes parameter for the reflected light, and varies with the refractive index of the surface layer and viewing angles. For the polarized detection of oil slicks, the highest sensitivity of the DOLP to the refractive index is located within the specular reflection direction where the sum of the solar and sensor zenith angles is 82.6°. The modeled results clearly indicate that the DOLP of oil slicks is weaker in comparison with oil-free seawater under sunglint. Using measurements from the space-borne Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) over the Deepwater Horizon oil spill in the Gulf of Mexico, we illustrate that the PARASOL-derived DOLP difference between the oil spill and seawater is obvious and is in accordance with the modeled results. These preliminary results suggest that the potential of multiangle measurement and feasibility of deriving refractive index of ocean surface from space-borne sensors need further researches. [ABSTRACT FROM AUTHOR]

Using fine spatial resolution (~ 7.6 m) hyperspectral AVIRIS data collected over the Deepwater Horizon oil spill in the Gulf of Mexico, we statistically estimated slick lengths, widths and length/width ratios to characterize oil slick morphology for different thickness classes. For all AVIRIS-detected oil slicks (N = 52,100 continuous features) binned into four thickness classes (≤ 50 μm but thicker than sheen, 50–200 μm, 200–1000 μm, and > 1000 μm), the median lengths, widths, and length/width ratios of these classes ranged between 22 and 38 m, 7–11 m, and 2.5–3.3, respectively. The AVIRIS data were further aggregated to 30-m (Landsat resolution) and 300-m (MERIS resolution) spatial bins to determine the fractional oil coverage in each bin. Overall, if 50% fractional pixel coverage were to be required to detect oil with thickness greater than sheen for most oil containing pixels, a 30-m resolution sensor would be needed. [ABSTRACT FROM AUTHOR]

The Ixtoc-I oil spill occurred in 1979 in shallow waters (50 m) of the Bay of Campeche, Mexico. Although it is known that a large portion of the released oil from this second largest accidental marine oil spill in history reached the surface, to date there has been no attempt to document the surface footprint and trajectory of the released oil. Our study attempts to fill this knowledge gap using remote sensing data collected by Landsat/MSS and CZCS. Both showed the same general patterns of oil trajectory to the northwest and north, nearly parallel to the coastline of the western Gulf of Mexico (GoM) with possible oil landing on Mexican and Texas beaches. Field observations at selected beaches and islands along the coast of the western and southern GoM during and after the spill confirmed these satellite-based findings, which were also used to help in planning a recent field campaign to collect sediment samples in the southern GoM. [ABSTRACT FROM AUTHOR]

Different oil spill pollution types could be produced in oil transport and weathering processes. Investigation of these pollution types is beneficial for oil spill recovery and processing. Optical remote sensing techniques play an important role in marine oil spill monitoring and have the ability to identify different oil spill pollution types. Recently, research on oil spill optical remote sensing has made much progress in detecting targets, identifying spectral response characteristics, and formulating theories. Floating black oil, oil slicks, and oil-water mixture in marine oil spill accidents are the main targets to be investigated by optical remote sensors. The visible spectral response differences of these targets are the base of oil spill optical remote sensing research. Bi-directional reflectance distribution function, light interference, absorption, and scattering of targets produce different spectra. Therefore, oil spill optical remote sensing could be used to identify the main oil spill pollution types and estimate oil spill volume. [ABSTRACT FROM AUTHOR]

Coastal reclamation in Guangdong Province is highly concentrated and is growing rapidly. However, intensive reclamation use has resulted in serious influence on the coastal ecosystem, directly and indirectly. The current conditions and spatial distribution of reclamations must be detected for coastal preservation and management using efficient technology. This study aims to find a suitable method and data to map reclamations accurately at a large scale. Pixel-based and object-oriented classification methods were applied in extracting the three typical types of coastal reclamation, namely, ports, aquaculture ponds, and salt pans, in Guangdong Province from Landsat 8 and Sentinel 2 images. The algorithms of a support vector machine, random forest, decision tree, and rule-based algorithm were performed. Classification results were compared with statistical measures to assess the performance of different algorithms. The results indicated that all of the algorithms could obtain classification results with high accuracy, whereas the object-oriented algorithm showed less efficiency than other algorithms in classifying ports with complicated features. High-resolution data were not always superior to lower-resolution data in the reclamation classification. Generally speaking, applying the rule-based object-oriented algorithm in Sentinel 2A MSI images is relatively efficient at detecting the reclamation use in coastal Guangdong considering its actual situation. The mapping of reclamations in the whole of coastal Guangdong shows that they present obvious agglomeration characteristics in the space. The aquaculture ponds are mainly distributed in the coastal zones of western Guangdong and eastern Guangdong, with the largest area of 77,963 ha. The other types of ports are mainly distributed in the coastal zones of the Pearl River Delta, with an area of 8146 ha, while salt pans are mainly distributed in the coastal zones of Jiangmen, Zhuhai, and Zhongshan, with a total area of 4072 ha. The results can provide key supporting data for decision making in coastal management and preservation. [ABSTRACT FROM AUTHOR]