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Synthetic aperture radar (SAR) has great potential for timely monitoring of flood information as it penetrates the clouds during flood events. Moreover, the proliferation of SAR satellites with high spatial and temporal resolution provides a tremendous opportunity to understand the flood risk and its quick response. However, traditional algorithms to extract flood inundation using SAR often require manual parameter tuning or data annotation, which presents a challenge for the rapid automated mapping of large and complex flooded scenarios. To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN. The algorithm consists of three phases: (i) super-pixel generation; (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation. We evaluated the Felz-CNN algorithm by mapping flood inundation during the Yangtze River flood in 2020, covering a total study area of 1,140,300 km2. When validated on fine-resolution Planet satellite imagery, the algorithm accurately identified flood extent with producer and user accuracy of 93% and 94%, respectively. The results are indicative of the usefulness of our unsupervised approach for the application of flood mapping. Meanwhile, we overlapped the post-disaster inundation map with a 10-m resolution global land cover map (FROM-GLC10) to assess the damages to different land cover types. Of these types, cropland and residential settlements were most severely affected, with inundation areas of 9,430.36 km2 and 1,397.50 km2, respectively, results that are in agreement with statistics from relevant agencies. Compared with traditional supervised classification algorithms that require time-consuming data annotation, our unsupervised algorithm can be deployed directly to high-performance computing platforms such as Google Earth Engine and PIE-Engine to generate a large-spatial map of flood-affected areas within minutes, without time-consuming data downloading and processing. Importantly, this efficiency enables the fast and effective monitoring of flood conditions to aid in disaster governance and mitigation globally.

The ability of superabsorbent polymers (SAPs) to absorb and retain a large amount of aqueous solution enables their applications in agriculture, medicine and water treatment. As a result, there were numerous studies reporting the present status and application prospects of raw materials and the mechanism of cross-linking agents. Conversely, there was a lack of research on the factors affecting SAP performance and the summaries of methods to improve performance. In this paper, a comprehensive and systematic review based on the structure and water absorption mechanism of SAPs was performed. The methods of improving the performance and main factors of the synthesis of SAPs, such as monomer, cross-linking agent, initiator, ion concentration, hydrophilic group, neutralization degree and particle size, were discussed. The improvement methods of salinity tolerance, reusability, water absorption and rate of SAP were explored. In addition, some applications and future research in SAPs are also discussed in this review.

The Commodity Futures Trading Commission (CFTC) recently identified large intra‐day price changes or “flash events” in continuously traded commodity futures markets. These flash events fueled discussion on whether futures markets are becoming less effective as human intervention is diminishing in favor of automated trading. Using intra‐day data, we examine liquidity resilience during “flash events” in corn and lean hog futures markets from 2014 to 2019. Overall, we find little evidence that the liquidity provision in these two markets relative to normal days becomes fragile when large price movements occur. Our analysis suggests that flash events are heavily influenced by unanticipated changes in fundamentals that may lead to a new equilibrium price. Liquidity dynamics during these events supports the view that active market making helps absorb the increased volume and stabilize markets.

With the availability of multi-sensor, multi-temporal, multi-resolution and multi-spectral images from operational Earth observation satellites, remote sensing image fusion has become a valuable tool. The goal of remote sensing image fusion is to integrate complementary information from multi-source data such that the new images are more suitable for human visual perception and computer-processing tasks such as segmentation, feature extraction, and object recognition. In this paper, a pixel-level remote sensing image fusion method is proposed, which is based on combining the principal component analysis (PCA) and the curvelet transformation (CT). First, the multi-spectral image with low-spatial-resolution is transformed by PCA and principal components are obtained. Second, the panchromatic image with high-spatial-resolution and the principal components of the multi-spectral image are respectively merged with the curvelet transform. Finally, the fused image is obtained by inverse CT and inverse PCA. The experiments using Landsat-8 OLI multi-spectral and panchromatic image show that, compared with the traditional methods such as the WT-based method, the IHS-based method, the HPF-based method, the BT-based method, the PCA-based method and the CT-based method, the results of the proposed method preserve the spatial details while preserving more spectral information of the original image.

Substantial controversies exist in the exploration of the molecular mechanism of heart failure (HF) and pose challenges to the diagnosis of HF and the discovery of specific drugs for the treatment. Recently, cardiac transthyretin (TTR) amyloidosis is becoming recognized as one of major causes of underdiagnosed HF. The investigation and modulation of TTR misfolding and amyloidal aggregation open up a new revenue to reveal the molecular mechanisms of HF and provide new possibilities for the treatment of HF. The aim of this review is to briefly introduce the recent advances in the study of TTR native and misfolding structures, discuss the correlation between the genotype and phenotype of cardiac TTR amyloidosis, and summarize the therapeutic applications of TTR structural stabilizers in the treatment of TTR amyloidosis-associated HF.

Chen, C.; He, X.; Lu, Y., and Chu, Y., 2020. Application of Landsat time-series data in island ecological environment monitoring: A case study of Zhoushan Islands, China. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 193–199. Coconut Creek (Florida), ISSN 0749-0208.An ecological environment is a complex system fundamental to the sustainable development of societies and economies. Landsat series satellites are widely used for monitoring the changes in Earth's surface morphology. With long time-series data, Landsat can provide new methods and technical support for eco-environmental monitoring. This study developed remote-sensing monitoring methods based on Landsat data to observe the changes in the ecological environment in the Zhoushan region, China. The regional environmental changes in vegetation, water, and other key ecological factors were analyzed using multitemporal remote-sensing data. This study shows the effectiveness of this method for the extraction of key eco-environmental factors such as vegetation and water body area and the evaluation of regional eco-environmental quality. This study also reveals ecological patterns in the Zhoushan region and provides data support for regional eco-environmental assessment and management.

Mountain forests cover a small fraction of the Earth’s surface, but may exert important influence on the hydrological cycles of river basins (e.g., evapotranspiration, river flow). Many montane ecosystems are currently experiencing forest loss or gain, due to direct land-use change and due to changes in climate. Previous studies revealed most deforestation and afforestation occur in the lowlands, while how forest cover changes at different altitudes in the mountains has not been fully understood. Here we present a study that aims to better understand the distribution of mountain forest change. We use a high-resolution global map of forest change during 2000-2018 combined with elevation data to complete a global analysis of the relationship of elevation with tree cover and tree cover loss and gain. We also assess which climate variables (temperature, rainfall, wind speed) might explain observed variations in tree cover. Our analysis provides new information on how and why mountain forests are changing.

Climate change and land‐use/cover change (LUCC) are two major types of global environmental change. They are increasingly challenging the main objectives of ecosystem management, which are to provide ecosystem services sustainably to society and maintain biodiversity. However, a comprehensive understanding of how climate–land‐use change affects these primary goals of ecosystem management is still lacking. Here, a global literature review on the impacts of climate change and LUCC on ecosystem services related to biodiversity is presented. In this review, possible ecological responses at species, community, and ecosystem levels, and the effects of interaction mechanisms between climate change and LUCC on biodiversity‐related ecosystem services are identified. The results show possible effects on species facing climate change challenges through affecting distribution/range shifts, interspecific relations, richness, and abundance, and the impacts on biodiversity through increasing extinction rates, nutrient deposition, and habitat fragmentation under LUCC. Climate change may hinder the ability of species to deal with LUCC, and in turn LUCC could reduce resilience to climate change. Understanding of these interactions is necessary to address the increasing pressure on sustainable provisioning of ecosystem services under different climate and land‐use scenarios in the future.

The frequent occurrence of red tides in China’s coastal areas has caused huge losses to society and economy, and seriously threatened the coastal marine environment and ecological security. Based on the data of 30 years from 1990 to 2019, this paper analyzes spatial-temporal distribution characteristics of red tide in China’s coastal areas. Our results show that 1557 red tides occurred in China’s coastal areas from 1990 to 2019. (1) There were 961 red tides from 2001 to 2012, which was the high red tide period in offshore China, accounting for 62.00% of the total number of red tides. The number of red tides in the Bohai Sea, Yellow Sea, East China Sea and South China Sea were 117, 103, 588 and 153, respectively. Seasonally, 536 red tides occurred from April to September, accounting for 87.00% of the total number of red tides. (2) In space, according to the kernel density estimation, red tides are mainly concentrated in Bohai Bay, Yangtze River Delta, Pearl River Delta and other regions with relatively dense population and relatively developed economy, especially in Zhejiang Province and the estuary of the Yangtze River. The research in this paper is of great significance to the monitoring and prevention of red tides and water environment management.

Stereo-vision-based measurement is affected by refraction of light in hydrodynamics and ocean-engineering experiments that are performed in glass flumes. Using the conventional 3D reconstruction strategy in measurement of underwater objects will lead to considerable errors. In this paper, a new reconstruction method based on ray tracing is proposed for measurement in the flume experiments. The refractive interfaces of the experiments are estimated by a robust algorithm, so that light rays corresponding to the image points can be recovered reliably. In the estimation algorithm, differential evolution is employed to compute globally optimal parameters from general image correspondences. To determine the more reasonable reconstructed points, a modified triangulation approach is also developed. The intersection of the back-projected rays is computed by correcting the corresponding image points. Experiments are conducted with synthetic data and real underwater objects. The results demonstrate the effectiveness and convenience of the proposed method in such underwater measurement tasks.

In the framework of multi-scale decomposition, a new multi-source image fusion algorithm based on principal component analysis (PCA) and curvelet transform is proposed for pixel-level image fusion. Firstly the low-resolution multi-spectral image is transformed by PCA and principal components are obtained. Secondly the high-resolution image and the first three principal components of the multi-spectral image are respectively merged with curvelet transform and finally the fused image is obtained by inverse PCA transform. This method gives the results of fusion and is compared with the traditional method of PCA, which proves the effectiveness of the method.

In this study, we investigated the pollution degree and spatial distribution of heavy metals and determined their sources in topsoil in a typical coal mine city, Lianyuan, Hunan Province, China. We collected 6078 soil surface samples in different land use types. And the concentrations of Zn, Cd, Cu, Hg, Pb, Sb, As, Mo, V, Mn, Fe and Cr were measured. The average contents of all heavy metals were lower than their corresponding Grade II values of Chinese Soil Quality Standard with the exception of Hg. However, average contents of twelve heavy metals, except for Mn, exceeded their background level in soils in Hunan Province. Based on one-way analysis of variance (ANOVA), the contents of Cu, Zn, Cd, Pb, Hg, Mo and V were related to the anthropogenic source and there were statistically significant differences in their concentrations among different land use patterns. The spatial variation of heavy metal was visualized by GIS. The PMF model was used to ascertain contamination sources of twelve heavy metals and apportion their source contributions in Lianyuan soils. The results showed that the source contributions of the natural source, atmospheric deposition, industrial activities and agricultural activities accounted for 33.6%, 26.05%, 23.44% and 16.91%, respectively.