Monitoring recent lake variations under climate change around the Altai Mountains using multimission satellite data

Estimating lake dynamics is vital for the accurate evaluation of climate change and water resources monitoring. However, it remains a challenge to estimate the lake mass budget due to extremely scarce in situ data, especially for alpine regions. In this article, multimission remote sensing observations were blended to examine recent lake variations and their responses to climate change around the Altai Mountains during 2001–2009 and 2010–1018. First, the multitemporal Landsat images were used to enable the detailed monitoring of the surface extent of 43 lakes (> 5 km 2 ) around the Altai Mountains from 2001 to 2018. The results presented that the total lake surface extent shrunk from 9835 km 2 in 2001 to a minimum of 9652 km 2 in 2009, while subsequently rose to 9714 km 2 in 2018. By combining the lake area with the water level derived from the ICESat and CryoSat-2 altimetry data, the water storage of seven lakes covering ∼84% of the overall lake area in the region was obtained. The total water storage was detected with a decrease of 4.86 ± 1.17 km 3 from 2003 to 2009 and a decrease of 3.65 ± 1.16 km 3 from 2010 to 2018, respectively. Although most of the glaciers in this region had a significant mass loss in the past decades, the factor analysis indicated that most of the lakes had maintained a steady or slightly changing tendency because the glacial melting water was counteracted by the negative impact of high evapotranspiration amount. For the lakes with a few glacier melting supplies, e.g., the Uvs lake and Hyargas lake, the significant water budget loss was caused by the increasing evapotranspiration, decreased precipitation, and developed animal husbandry, which mainly dominated the overall decreasing trend of lake water storage in the Altai Mountains

A Comparison of Sentinel-1 Biased and Unbiased Coherence for Crop Monitoring and Classification

Synthetic Aperture Radar (SAR) holds significant potential for applications in crop monitoring and classification. Interferometric SAR (InSAR) coherence proves effective in monitoring crop growth. Currently, the coherence based on the maximum likelihood estimator is biased towards low coherence values. Therefore, the main aim of this work is to access the performance of Sentinel-1 time-series biased coherence and unbiased coherence in crop monitoring and classification. This study was conducted during the 2018 growing season (April-October) in Komoka, an agricultural region in southwestern Ontario, Canada, primarily cultivating three crops: soybean, corn, and winter wheat. To verify the ability of coherence to monitor crops, a linear correlation coefficient between temporal coherence and dual polarimetric radar vegetation index (DpRVI) was fitted. The results revealed a stable correlation between temporal coherence and DpRVI time-series, with the highest correlation observed for soybean (0.7 < R < 0.8), followed by wheat and corn. Notably, unbiased coherence of the VV channel exhibited the highest correlation (R > 0.75). In addition, we applied unbiased coherence to crop classification. The results show that unbiased coherence exhibits very promising classification performance, with the overall accuracy (84.83%) and kappa coefficient (0.76) of VV improved by 8.35% and 0.12, respectively, over biased coherence, and the overall accuracy (73.25%) and kappa coefficient (0.57) of VH improved by 7.56% and 0.14, respectively, over biased coherence, and all crop classification accuracies were also effectively improved. This study demonstrates the feasibility of coherence monitoring of crops and provides new insights in enhancing the higher separability of crops

Effects of Ultrafine Grinding Pretreatment on Physicochemical Properties of Alkali- and Water-Extracted Wheat Bran Polysaccharides

To utilize wheat bran by-products in high value, wheat bran raw materials were pretreated with ultrafine grinding (for 0, 10, 20, and 30 min) and wheat bran polysaccharide was obtained by water extraction and alkali extraction. The effects of ultrafine grinding pretreatment on the physicochemical properties of wheat bran polysaccharides were analyzed based on yield, chemical composition (arabinoxylan, total sugar, protein, and ferulic acid), infrared spectrum, monosaccharide composition, potential, particle size, solubility, and microstructure. The results showed that the yield of alkali-extracted and water-extracted wheat bran polysaccharide increased from 6.6% and 1.34% to 15.03% and 6.28%, respectively. The content of arabxylan (AX) obtained by alkali extraction and water extraction increased from 53.13% and 33.32% to 73.35% and 37.52%, respectively. The particle size of alkali-extracted and water-extracted wheat bran polysaccharide decreased from 308.47 and 919.23 nm to 203.8 and 168.03 nm, respectively. The results from infrared spectroscopy and monosaccharide composition analysis showed that the ultrafine grinding pretreatment had little effect on the structure of the functional group of polysaccharide extracted from wheat bran by alkali and water extraction. However, the pretreatment could destroy the ordered structure of the wheat bran polysaccharide. The total content and proportion of arabinose and xylose, which were the main components of wheat bran polysaccharide, increased significantly after ultrafine grinding (P<0.05). The scanning electron microscopic (SEM) data showed that the particle shape of the polysaccharide changed from large to small and there was high adhesion between polysaccharide. Taken together, the ultrafine grinding pretreatment can improve the yield of wheat bran polysaccharide and enhance the content of Arabxylan, while reducing the particle size of the polysaccharide

Temperature Variability and Mortality: A Multi-Country Study

Background: The evidence and method are limited for the associations between mortality and temperature variability (TV) within or between days. Objectives: We developed a novel method to calculate TV and investigated TV-mortality associations using a large multicountry data set. Methods: We collected daily data for temperature and mortality from 372 locations in 12 countries/regions (Australia, Brazil, Canada, China, Japan, Moldova, South Korea, Spain, Taiwan, Thailand, the United Kingdom, and the United States). We calculated TV from the standard deviation of the minimum and maximum temperatures during the exposure days. Two-stage analyses were used to assess the relationship between TV and mortality. In the first stage, a Poisson regression model allowing over-dispersion was used to estimate the community-specific TV-mortality relationship, after controlling for potential confounders. In the second stage, a meta-analysis was used to pool the effect estimates within each country. Results: There was a significant association between TV and mortality in all countries, even after controlling for the effects of daily mean temperature. In stratified analyses, TV was still significantly associated with mortality in cold, hot, and moderate seasons. Mortality risks related to TV were higher in hot areas than in cold areas when using short TV exposures (0–1 days), whereas TV-related mortality risks were higher in moderate areas than in cold and hot areas when using longer TV exposures (0–7 days). Conclusions: The results indicate that more attention should be paid to unstable weather conditions in order to protect health. These findings may have implications for developing public health policies to manage health risks of climate change. Citation: Guo Y, Gasparrini A, Armstrong BG, Tawatsupa B, Tobias A, Lavigne E, Coelho MS, Pan X, Kim H, Hashizume M, Honda Y, Guo YL, Wu CF, Zanobetti A, Schwartz JD, Bell ML, Overcenco A, Punnasiri K, Li S, Tian L, Saldiva P, Williams G, Tong S. 2016. Temperature variability and mortality: a multi-country study. Environ Health Perspect 124:1554–1559; http://dx.doi.org/10.1289/EHP14

Heat wave and mortality: A multicountry, multicommunity study

Background: Few studies have examined variation in the associations between heat waves and mortality in an international context. Objectives: We aimed to systematically examine the impacts of heat waves on mortality with lag effects internationally. Methods: We collected daily data of temperature and mortality from 400 communities in 18 countries/regions and defined 12 types of heat waves by combining community-specific daily mean temperature ≥90th, 92.5th, 95th, and 97.5th percentiles of temperature with duration ≥2, 3, and 4 d. We used time-series analyses to estimate the community-specific heat wave–mortality relation over lags of 0–10 d. Then, we applied meta-analysis to pool heat wave effects at the country level for cumulative and lag effects for each type of heat wave definition. Results: Heat waves of all definitions had significant cumulative associations with mortality in all countries, but varied by community. The higher the temperature threshold used to define heat waves, the higher heat wave associations on mortality. However, heat wave duration did not modify the impacts. The association between heat waves and mortality appeared acutely and lasted for 3 and 4 d. Heat waves had higher associations with mortality in moderate cold and moderate hot areas than cold and hot areas. There were no added effects of heat waves on mortality in all countries/regions, except for Brazil, Moldova, and Taiwan. Heat waves defined by daily mean and maximum temperatures produced similar heat wave–mortality associations, but not daily minimum temperature. Conclusions: Results indicate that high temperatures create a substantial health burden, and effects of high temperatures over consecutive days are similar to what would be experienced if high temperature days occurred independently. People living in moderate cold and moderate hot areas are more sensitive to heat waves than those living in cold and hot areas. Daily mean and maximum temperatures had similar ability to define heat waves rather than minimum temperature.Medical Research Council, U

Distorted Building Image Matching with Automatic Viewpoint Rectification and Fusion

Building image-matching plays a critical role in the urban applications. However, finding reliable and sufficient feature correspondences between the real-world urban building images that were captured in widely separate views are still challenging. In this paper, we propose a distorted image matching method combining the idea of viewpoint rectification and fusion. Firstly, the distorted images are rectified to the standard view with the transform invariant low-rank textures (TILT) algorithm. A local symmetry feature graph is extracted from the building images, followed by multi-level clustering using the mean shift algorithm, to automatically detect the low-rank texture region. After the viewpoint rectification, the Oriented FAST and Rotated BRIEF (ORB) feature is used to match the images. The grid-based motion statistics (GMS) and RANSAC techniques are introduced to remove the outliers and preserve the correct matching points to deal with the mismatched pairs. Finally, the matching results for the rectified views are projected to the original viewpoint space, and the matches before and after distortion rectification are fused to further determine the final matches. The experimental results show that both the number of matching pairs and the matching precision for the distorted building images can be significantly improved while using the proposed method

Automatic Semi-Global Artificial Shoreline Subpixel Localization Algorithm for Landsat Imagery

Shoreline mapping using satellite remote sensing images has the advantages of large-scale surveys and high efficiency. However, low spatial resolution, various geometric morphologies and complex offshore environments prevent accurate positioning of the shoreline. This article proposes a semi-global subpixel shoreline localization method that considers utilizing morphological control points to divide the initial artificial shoreline into segments of relatively simple morphology and analyzing the local intensity homogeneity to calculate the intensity integral error. Combined with the segmentation-merge-fitting method, the algorithm determines the subpixel location accurately. In experiments, we select five artificial shorelines with various geometric morphologies from Landsat 8 Operational Land Imager (OLI) data. The five subpixel artificial shoreline RMSE results lie in the range of 3.02 m to 4.77 m, with line matching results varying from 2.51 m to 3.72 m. Thus, it can be concluded that the proposed subpixel localization algorithm is effective and applicable to artificial shoreline in various geometric morphologies and is robust to complex offshore environments, to some extent