Spatiotemporal Pattern of FeNi Metal Melt Solidification and Crystallization Mechanism in Space

The subtropical vegetation plays an important role in maintaining the structure and function of global ecosystems, and its contribution to the global carbon balance are receiving increasing attention. The fractional vegetation cover (FVC) as an important indicator for monitoring environment change, is widely used to analyze the spatiotemporal pattern of regional and even global vegetation. China is an important distribution area of subtropical vegetation. Therefore, we first used the dimidiate pixel model to extract the subtropical FVC of China during 2001–2018 based on MODIS land surface reflectance data, and then used the linear regression analysis and the variation coefficient to explore its spatiotemporal variations characteristics. Finally, the partial correlation analysis and the partial derivative model were used to analyze the influences and contributions of climate factors on FVC, respectively. The results showed that (1) the subtropical FVC had obvious spatiotemporal heterogeneity; the FVC high-coverage and medium-coverage zones were concentratedly and their combined area accounted for more than 70% of the total study area. (2) The interannual variation in the average subtropical FVC from 2001 to 2018 showed a significant growth trend. (3) In 76.28% of the study area, the regional FVC showed an increasing trend, and the remaining regional FVC showed a decreasing trend. However, the overall fluctuations in the FVC (increasing or decreasing) in the region were relatively stable. (4) The influences of climate factors to the FVC exhibited obvious spatial differences. More than half of all pixels exhibited the influence of the average annual minimum temperature and the annual precipitation had positive on FVC, while the average annual maximum temperature had negative on FVC. (5) The contributions of climate changes to FVC had obvious heterogeneity, and the average annual minimum temperature was the main contribution factor affecting the dynamic variations of FVC.

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Crystallization mechanism of Zr55Cu30Al10Ni5 metallic glass in an extended range of heating rates

Intermetallics ◽

10.1016/j.intermet.2021.107256 ◽

2021 ◽

Vol 136 ◽

pp. 107256

Author(s):

Yuanyuan Zhang ◽

Xin Lin ◽

Xuehao Gao ◽

Xiaolei Su ◽

Shengbao Luo ◽

...

Keyword(s):

Metallic Glass ◽

Crystallization Mechanism ◽

Heating Rates ◽

Extended Range

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Spatiotemporal Pattern of Pine Wilt Disease in the Yangtze River Basin

Forests ◽

10.3390/f12060731 ◽

2021 ◽

Vol 12 (6) ◽

pp. 731

Author(s):

Zhuoqing Hao ◽

Jixia Huang ◽

Yantao Zhou ◽

Guofei Fang

Keyword(s):

River Basin ◽

Yangtze River ◽

Yangtze River Basin ◽

Pine Wilt Disease ◽

Wilt Disease ◽

Spatiotemporal Pattern ◽

Pinewood Nematode ◽

The Yangtze River ◽

Pine Wilt ◽

The Yangtze River Basin

The Yangtze River Basin is among the river basins with the strongest strategic support and developmental power in China. As an invasive species, the pinewood nematode (PWN) Bursaphelenchus xylophilus has introduced a serious obstacle to the high-quality development of the economic and ecological synchronization of the Yangtze River Basin. This study analyses the occurrence and spread of pine wilt disease (PWD) with the aim of effectively managing and controlling the spread of PWD in the Yangtze River Basin. In this study, statistical data of PWD-affected areas in the Yangtze River Basin are used to analyse the occurrence and spread of PWD in the study area using spatiotemporal visualization analysis and spatiotemporal scanning statistics technology. From 2000 to 2018, PWD in the study area showed an “increasing-decreasing-increasing” trend, and PWD increased explosively in 2018. The spatial spread of PWD showed a “jumping propagation-multi-point outbreak-point to surface spread” pattern, moving west along the river. Important clusters were concentrated in the Jiangsu-Zhejiang area from 2000 to 2015, forming a cluster including Jiangsu and Zhejiang. Then, from 2015–2018, important clusters were concentrated in Chongqing. According to the spatiotemporal scanning results, PWD showed high aggregation in the four regions of Zhejiang, Chongqing, Hubei, and Jiangxi from 2000 to 2018. In the future, management systems for the prevention and treatment of PWD, including ecological restoration programs, will require more attention.

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Impact of Monsoon-Transported Anthropogenic Aerosols and Sun-Glint on the Satellite-Derived Spectral Remote Sensing Reflectance in the Indian Ocean

Remote Sensing ◽

10.3390/rs13020184 ◽

2021 ◽

Vol 13 (2) ◽

pp. 184

Author(s):

Rongjie Liu ◽

Jie Zhang ◽

Tingwei Cui ◽

Haocheng Yu

Keyword(s):

Remote Sensing ◽

Indian Ocean ◽

Infrared Imaging ◽

Atmospheric Correction ◽

Spatiotemporal Pattern ◽

Anthropogenic Aerosols ◽

Remote Sensing Reflectance ◽

The Indian Ocean ◽

Moderate Resolution Imaging Spectroradiometer ◽

Terra Modis

Spectral remote sensing reflectance (Rrs(λ), sr−1) is one of the most important products of ocean color satellite missions, where accuracy is essential for retrieval of in-water, bio-optical, and biogeochemical properties. For the Indian Ocean (IO), where Rrs(λ) accuracy has not been well documented, the quality of Rrs(λ) products from Moderate Resolution Imaging Spectroradiometer onboard both Terra (MODIS-Terra) and Aqua (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-Orbiting Partnership spacecraft (VIIRS-NPP), is evaluated and inter-compared based on a quality assurance (QA) system, which can objectively grade each individual Rrs(λ) spectrum, with 1 for a perfect spectrum and 0 for an unusable spectrum. Taking the whole year of 2016 as an example, spatiotemporal pattern of Rrs(λ) quality in the Indian Ocean is characterized for the first time, and the underlying factors are elucidated. Specifically, QA analysis of the monthly Rrs(λ) over the IO indicates good quality with the average scores of 0.93 ± 0.02, 0.92 ± 0.02 and 0.92 ± 0.02 for VIIRS-NPP, MODIS-Aqua, and MODIS-Terra, respectively. Low-quality (~0.7) data are mainly found in the Bengal Bay (BB) from January to March, which can be attributed to the imperfect atmospheric correction due to anthropogenic absorptive aerosols transported by the northeasterly winter monsoon. Moreover, low-quality (~0.74) data are also found in the clear oligotrophic gyre zone (OZ) of the south IO in the second half of the year, possibly due to residual sun-glint contributions. These findings highlight the effects of monsoon-transported anthropogenic aerosols, and imperfect sun-glint removal on the Rrs(λ) quality. Further studies are advocated to improve the sun-glint correction in the oligotrophic gyre zone and aerosol correction in the complex ocean–atmosphere environment.

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