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Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across
, 2014
"... Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are th ..."
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Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target
Multiple Cropping Intensity in China Derived from Agro-meteorolo- gical Observations and MODIS Data
"... Abstract: Double- and triple-cropping in a year have played a very important role in meeting the rising need for food in China. How-ever, the intensified agricultural practices have significantly altered biogeochemical cycles and soil quality. Understanding and mapping cropping intensity in China′s ..."
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Abstract: Double- and triple-cropping in a year have played a very important role in meeting the rising need for food in China. How-ever, the intensified agricultural practices have significantly altered biogeochemical cycles and soil quality. Understanding and mapping cropping intensity in China′s agricultural systems are therefore necessary to better estimate carbon, nitrogen and water fluxes within agro-ecosystems on the national scale. In this study, we investigated the spatial pattern of crop calendar and multiple cropping rotations in China using phenological records from 394 agro-meteorological stations (AMSs) across China. The results from the analysis of in situ field observations were used to develop a new algorithm that identifies the spatial distribution of multiple cropping in China from mod-erate resolution imaging spectroradiometer (MODIS) time series data with a 500 m spatial resolution and an 8-day temporal resolution. According to the MODIS-derived multiple cropping distribution in 2002, the proportion of cropland cultivated with multiple crops
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"... 2010). Rice paddy fields account for over 11 % of global cropland area, and produce food for more than half of the world’s popula-tion, especially in monsoon Asia (FAOSTAT, 2009). The continuous increase of paddy rice production (Matsumura et al., 2009), which is mainly attributed to the expansion o ..."
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2010). Rice paddy fields account for over 11 % of global cropland area, and produce food for more than half of the world’s popula-tion, especially in monsoon Asia (FAOSTAT, 2009). The continuous increase of paddy rice production (Matsumura et al., 2009), which is mainly attributed to the expansion of rice paddy fields, increased since the Green wing global pop-on is proje poses hug sure on food security (FAO, 2009). As the major rice-pro region, Asia comprises approximately 90 % of the global rice h area and production, and residents there obtain over 35 % o daily calories from rice. As rice paddy fields are flooded during most of the growing period, the expansion of rice paddy fields can further exacerbate water shortages, threatening ecosystem sustainability and services (Bouman et al., 2007), especially in Asia, where agriculture irrigation accounts for over 80 % of total water use (FAOSTAT, 2009). Rice paddy fields have also been iden-tified as an important source of global atmospheric methane (CH4)
Article A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)
"... www.mdpi.com/journal/remotesensing ..."
COTTON PLANTING AREA EXTRACTION BASED ON MULTI-TEMPORAL LANDSAT8 IMAGES
"... ABSTRACT: Crop classification plays an important role in effective and controllable agricultural management. In this paper, nine scenes of Landsat8_OLI data in 2013 were collected for cotton planting area extraction in Shawan country of Xinjiang Uygur Autonomous Region, China. Normalized Difference ..."
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ABSTRACT: Crop classification plays an important role in effective and controllable agricultural management. In this paper, nine scenes of Landsat8_OLI data in 2013 were collected for cotton planting area extraction in Shawan country of Xinjiang Uygur Autonomous Region, China. Normalized Difference Vegetation Index (NDVI) time series were generated to characterize the phenological pattern of each crop type. The optimal temporal reflectance image was chosen by analyzing the difference of NDVI profile between cotton and other crop types. The hierarchical classification strategy was performed on the three features of NDVI time series, NDVI statistics and reflectance. Firstly a simple decision tree was built on NDVI statistics and reflectance to extract vegetation cover; then various types of crops were distinguished by support vector machine (SVM) and maximum likelihood supervised classifier (MLC), thereby cotton plating area was extracted. A comprehensive evaluation for the cotton extraction map was performed using human-computer interaction visual interpretation and ground truth data. Results showed that MLC achieved the accuracy of 97.56 % for cotton extraction. The cotton extraction map was very consistent with the ground truth data. This multi-temporal classification method is promising for crop extraction even for land cover classification. 1.
