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Yuhan Rao   Mr.  Graduate Student or Post Graduate 
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Yuhan Rao published an article in December 2015.
Top co-authors
Jin Chen

120 shared publications

State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Xuehong Chen

48 shared publications

State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China

Yasushi Yamaguchi

43 shared publications

Graduate School of Environmental studies, Nagoya University, D2-1 (510) Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan

Xiao-Lin Zhu

40 shared publications

The Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, People’s Republic of China

Publication Record
Distribution of Articles published per year 
(2013 - 2015)
Total number of journals
published in
Publications See all
Article 0 Reads 4 Citations Identification of climate factors related to human infection with avian influenza A H7N9 and H5N1 viruses in China Jing Li, Yuhan Rao, Qinglan Sun, Xiaoxu Wu, Jiao Jin, Yuhai ... Published: 11 December 2015
Scientific Reports, doi: 10.1038/srep18094
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Human influenza infections display a strongly seasonal pattern. However, whether H7N9 and H5N1 infections correlate with climate factors has not been examined. Here, we analyzed 350 cases of H7N9 infection and 47 cases of H5N1 infection. The spatial characteristics of these cases revealed that H5N1 infections mainly occurred in the South, Middle, and Northwest of China, while the occurrence of H7N9 was concentrated in coastal areas of East and South of China. Aside from spatial-temporal characteristics, the most adaptive meteorological conditions for the occurrence of human infections by these two viral subtypes were different. We found that H7N9 infections correlate with climate factors, especially temperature (TEM) and relative humidity (RHU), while H5N1 infections correlate with TEM and atmospheric pressure (PRS). Hence, we propose a risky window (TEM 4–14 °C and RHU 65–95%) for H7N9 infection and (TEM 2–22 °C and PRS 980-1025 kPa) for H5N1 infection. Our results represent the first step in determining the effects of climate factors on two different virus infections in China and provide warning guidelines for the future when provinces fall into the risky windows. These findings revealed integrated predictive meteorological factors rooted in statistic data that enable the establishment of preventive actions and precautionary measures against future outbreaks.
Article 0 Reads 18 Citations An Improved Method for Producing High Spatial-Resolution NDVI Time Series Datasets with Multi-Temporal MODIS NDVI Data a... Yuhan Rao, Xiaolin Zhu, Jin Chen, Jianmin Wang Published: 16 June 2015
Remote Sensing, doi: 10.3390/rs70607865
DOI See at publisher website ABS Show/hide abstract
Due to technical limitations, it is impossible to have high resolution in both spatial and temporal dimensions for current NDVI datasets. Therefore, several methods are developed to produce high resolution (spatial and temporal) NDVI time-series datasets, which face some limitations including high computation loads and unreasonable assumptions. In this study, an unmixing-based method, NDVI Linear Mixing Growth Model (NDVI-LMGM), is proposed to achieve the goal of accurately and efficiently blending MODIS NDVI time-series data and multi-temporal Landsat TM/ETM+ images. This method firstly unmixes the NDVI temporal changes in MODIS time-series to different land cover types and then uses unmixed NDVI temporal changes to predict Landsat-like NDVI dataset. The test over a forest site shows high accuracy (average difference: −0.0070; average absolute difference: 0.0228; and average absolute relative difference: 4.02%) and computation efficiency of NDVI-LMGM (31 seconds using a personal computer). Experiments over more complex landscape and long-term time-series demonstrated that NDVI-LMGM performs well in each stage of vegetation growing season and is robust in regions with contrasting spatial and spatial variations. Comparisons between NDVI-LMGM and current methods (i.e., Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced STARFM (ESTARFM) and Weighted Linear Model (WLM)) show that NDVI-LMGM is more accurate and efficient than current methods. The proposed method will benefit land surface process research, which requires a dense NDVI time-series dataset with high spatial resolution.
Article 0 Reads 19 Citations Temperature sensitivity of spring vegetation phenology correlates to within-spring warming speed over the Northern Hemis... Cong Wang, Ruyin Cao, Jin Chen, Yuhan Rao, Yanhong Tang Published: 01 March 2015
Ecological Indicators, doi: 10.1016/j.ecolind.2014.11.004
DOI See at publisher website
Article 0 Reads 6 Citations A Method for Screening Climate Change-Sensitive Infectious Diseases Yunjing Wang, Yuhan Rao, Xiaoxu Wu, Hainan Zhao, Jin Chen, S... Published: 14 January 2015
International Journal of Environmental Research and Public Health, doi: 10.3390/ijerph120100767
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Climate change is a significant and emerging threat to human health, especially where infectious diseases are involved. Because of the complex interactions between climate variables and infectious disease components (i.e., pathogen, host and transmission environment), systematically and quantitatively screening for infectious diseases that are sensitive to climate change is still a challenge. To address this challenge, we propose a new statistical indicator, Relative Sensitivity, to identify the difference between the sensitivity of the infectious disease to climate variables for two different climate statuses (i.e., historical climate and present climate) in non-exposure and exposure groups. The case study in Anhui Province, China has demonstrated the effectiveness of this Relative Sensitivity indicator. The application results indicate significant sensitivity of many epidemic infectious diseases to climate change in the form of changing climatic variables, such as temperature, precipitation and absolute humidity. As novel evidence, this research shows that absolute humidity has a critical influence on many observed infectious diseases in Anhui Province, including dysentery, hand, foot and mouth disease, hepatitis A, hemorrhagic fever, typhoid fever, malaria, meningitis, influenza and schistosomiasis. Moreover, some infectious diseases are more sensitive to climate change in rural areas than in urban areas. This insight provides guidance for future health inputs that consider spatial variability in response to climate change.
Article 0 Reads 8 Citations Earlier vegetation green-up has reduced spring dust storms Bihang Fan, Li Guo, Ning Li, Jin Chen, Henry Lin, Xiaoyang Z... Published: 24 October 2014
Scientific Reports, doi: 10.1038/srep06749
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
The observed decline of spring dust storms in Northeast Asia since the 1950s has been attributed to surface wind stilling. However, spring vegetation growth could also restrain dust storms through accumulating aboveground biomass and increasing surface roughness. To investigate the impacts of vegetation spring growth on dust storms, we examine the relationships between recorded spring dust storm outbreaks and satellite-derived vegetation green-up date in Inner Mongolia, Northern China from 1982 to 2008. We find a significant dampening effect of advanced vegetation growth on spring dust storms (r = 0.49, p = 0.01), with a one-day earlier green-up date corresponding to a decrease in annual spring dust storm outbreaks by 3%. Moreover, the higher correlation (r = 0.55, p < 0.01) between green-up date and dust storm outbreak ratio (the ratio of dust storm outbreaks to times of strong wind events) indicates that such effect is independent of changes in surface wind. Spatially, a negative correlation is detected between areas with advanced green-up dates and regional annual spring dust storms (r = −0.49, p = 0.01). This new insight is valuable for understanding dust storms dynamics under the changing climate. Our findings suggest that dust storms in Inner Mongolia will be further mitigated by the projected earlier vegetation green-up in the warming world.
Article 1 Read 9 Citations A Combination of TsHARP and Thin Plate Spline Interpolation for Spatial Sharpening of Thermal Imagery Xuehong Chen, Wentao Li, Jin Chen, Yuhan Rao, Yasushi Yamagu... Published: 28 March 2014
Remote Sensing, doi: 10.3390/rs6042845
DOI See at publisher website ABS Show/hide abstract
There have been many studies and much attention paid to spatial sharpening for thermal imagery. Among them, TsHARP, based on the good correlation between vegetation index and land surface temperature (LST), is regarded as a standard technique because of its operational simplicity and effectiveness. However, as LST is affected by other factors (e.g., soil moisture) in the areas with low vegetation cover, these areas cannot be well sharpened by TsHARP. Thin plate spline (TPS) is another popular downscaling technique for surface data. It has been shown to be accurate and robust for different datasets; however, it has not yet been attempted in thermal sharpening. This paper proposes to combine the TsHARP and TPS methods to enhance the advantages of each. The spatially explicit errors of these two methods were firstly estimated in theory, and then the results of TPS and TsHARP were combined with the estimation of their errors. The experiments performed across various landscapes and data showed that the proposed combined method performs more robustly and accurately than TsHARP.