Showing papers by "Xiangming Xiao published in 2017"

A global moderate resolution dataset of gross primary production of vegetation for 2000–2016

Abstract: Accurate estimation of the gross primary production (GPP) of terrestrial vegetation is vital for understanding the global carbon cycle and predicting future climate change. Multiple GPP products are currently available based on different methods, but their performances vary substantially when validated against GPP estimates from eddy covariance data. This paper provides a new GPP dataset at moderate spatial (500 m) and temporal (8-day) resolutions over the entire globe for 2000-2016. This GPP dataset is based on an improved light use efficiency theory and is driven by satellite data from MODIS and climate data from NCEP Reanalysis II. It also employs a state-of-the-art vegetation index (VI) gap-filling and smoothing algorithm and a separate treatment for C3/C4 photosynthesis pathways. All these improvements aim to solve several critical problems existing in current GPP products. With a satisfactory performance when validated against in situ GPP estimates, this dataset offers an alternative GPP estimate for regional to global carbon cycle studies.

A mangrove forest map of China in 2015: analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform.

Abstract: Due to rapid losses of mangrove forests caused by anthropogenic disturbances and climate change, accurate and contemporary maps of mangrove forests are needed to understand how mangrove ecosystems are changing and establish plans for sustainable management. In this study, a new classification algorithm was developed using the biophysical characteristics of mangrove forests in China. More specifically, these forests were mapped by identifying: (1) greenness, canopy coverage, and tidal inundation from time series Landsat data, and (2) elevation, slope, and intersection-with-sea criterion. The annual mean Normalized Difference Vegetation Index (NDVI) was found to be a key variable in determining the classification thresholds of greenness, canopy coverage, and tidal inundation of mangrove forests, which are greatly affected by tide dynamics. In addition, the integration of Sentinel-1A VH band and modified Normalized Difference Water Index (mNDWI) shows great potential in identifying yearlong tidal and fresh water bodies, which is related to mangrove forests. This algorithm was developed using 6 typical Regions of Interest (ROIs) as algorithm training and was run on the Google Earth Engine (GEE) cloud computing platform to process 1941 Landsat images (25 Path/Row) and 586 Sentinel-1A images circa 2015. The resultant mangrove forest map of China at 30 m spatial resolution has an overall/users/producer’s accuracy greater than 95% when validated with ground reference data. In 2015, China’s mangrove forests had a total area of 20,303 ha, about 92% of which was in the Guangxi Zhuang Autonomous Region, Guangdong, and Hainan Provinces. This study has demonstrated the potential of using the GEE platform, time series Landsat and Sentine-1A SAR images to identify and map mangrove forests along the coastal zones. The resultant mangrove forest maps are likely to be useful for the sustainable management and ecological assessments of mangrove forests in China.

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Abstract: Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

Automatic land cover classification of geo-tagged field photos by deep learning

Abstract: With more and more crowdsourcing geo-tagged field photos available online, they are becoming a potentially valuable source of information for environmental studies. However, the labelling and recognition of these photos are time-consuming. To utilise such information, a land cover type recognition model for field photos was proposed based on the deep learning technique. This model combines a pre-trained convolutional neural network (CNN) as the image feature extractor and the multinomial logistic regression model as the feature classifier. The pre-trained CNN model Inception-v3 was used in this study. The labelled field photos from the Global Geo-Referenced Field Photo Library ( http://eomf.ou.edu/photos ) were chosen for model training and validation. The results indicated that our recognition model achieved an acceptable accuracy (48.40% for top-1 prediction and 76.24% for top-3 prediction) of land cover classification. With accurate self-assessment of confidence, the model can be applied to classify numerous online geo-tagged field photos for environmental information extraction.

Dominant role of plant physiology in trend and variability of gross primary productivity in North America

Abstract: Annual gross primary productivity (GPP) varies considerably due to climate-induced changes in plant phenology and physiology. However, the relative importance of plant phenology and physiology on annual GPP variation is not clear. In this study, a Statistical Model of Integrated Phenology and Physiology (SMIPP) was used to evaluate the relative contributions of maximum daily GPP (GPPmax) and the start and end of growing season (GSstart and GSend) to annual GPP variability, using a regional GPP product in North America during 2000-2014 and GPP data from 24 AmeriFlux sites. Climatic sensitivity of the three indicators was assessed to investigate the climate impacts on plant phenology and physiology. The SMIPP can explain 98% of inter-annual variability of GPP over mid- and high latitudes in North America. The long-term trend and inter-annual variability of GPP are dominated by GPPmax both at the ecosystem and regional scales. During warmer spring and autumn, GSstart is advanced and GSend delayed, respectively. GPPmax responds positively to summer temperature over high latitudes (40-80°N), but negatively in mid-latitudes (25-40°N). This study demonstrates that plant physiology, rather than phenology, plays a dominant role in annual GPP variability, indicating more attention should be paid to physiological change under futher climate change.

Status of land use intensity in China and its impacts on land carrying capacity

Abstract: Land use intensity quantifies the impacts of human activities on natural ecosystems, which have become the major driver of global environmental change, and thus it serves as an essential measurement for assessing land use sustainability. To date, land-change studies have mainly focused on changes in land cover and their effects on ecological processes, whereas land use intensity has not yet received the attention it deserves and for which spatially-explicit representation studies have only just begun. In this paper, according to the degree and reversibility of surface disturbance by human activities, there are four main classes of land use intensity: artificial land, semi-artificial land, semi-natural land, and natural land. These were further divided into 22 subclasses based on key indicators, such as human population density and the cropping intensity. Land use intensity map of China at a 1-km spatial resolution was obtained based on satellite images and statistical data. The area proportions of artificial land, semi-artificial land, semi-natural land, and natural land were 0.71%, 19.36%, 58.93%, and 21%, respectively. Human and economic carrying capacity increased with the increase of land use intensity. Artificial land supports 24.58% and 35.62% of the total population and GDP, using only 0.71% of the total land, while semi-artificial land supported 58.24% and 49.61% of human population and GDP with 19.36% of China’s total land area.

Annual Dynamics of Forest Areas in South America during 2007-2010 at 50-m Spatial Resolution

Abstract: South America has the largest tropical rainforests and the richest biodiversity in the world. It is challenging to map tropical forests and their spatio-temporal changes because forests are facing fragmentation from human activities (e.g., logging, deforestation), drought, and fire, as well as persistent clouds. Here we present a robust approach to map forests in South America during 2007–2010 and analyze the consistency and uncertainty among eight major forest maps in South America. Greenness-relevant MOD13Q1 NDVI and structure/biomass-relevant ALOS PALSAR time series data recorded 2007 through 2010 were coupled to identify and map forests at 50-m spatial resolution. Both area and spatial comparison were conducted to analyze the consistency and uncertainty of these eight forest maps. Annual 50-m PALSAR/MODIS forest maps were generated during 2007–2010 and the total forest area in South America was about 8.63 × 10 6 km 2 in 2010. Large differences in total forest area (8.2 × 10 6 km 2 –12.7 × 10 6 km 2 ) existed among these forest products, especially in the forest edges, semi-humid tropical, and subtropical regions. Forest products generated under a similar forest definition had similar or even larger variation than those generated with contrasting forest definitions. We also find out that one needs to consider leaf area index as an adjusting factor and use much higher threshold values in the Vegetation Continuous Field (VCF) datasets to estimate forest cover areas. Analyses of PALSAR/MODIS forest maps in 2008/2009 showed a relatively small rate of loss (3.2 × 10 4 km 2 year − 1 ) in net forest cover, similar to that of FAO FRA (3.3 × 10 4 km 2 year − 1 ), but much higher annual rates of forest loss and gain. The rate of forest loss (0.195 × 10 6 km 2 year − 1 ) was higher than that of Global Forest Watch (0.081 × 10 6 km 2 year − 1 ). PALSAR/MODIS forest maps showed that more deforestation occurred in the unfragmented forest areas. Caution should be used when using the different forest maps to analyze forest loss and make policies regarding forest ecosystem services and biodiversity conservation. The integration of PALSAR and MODIS images during 2007–2010 provides annual maps of forests in South America with improved accuracy and reduced uncertainty.

Quantifying annual changes in built-up area in complex urban-rural landscapes from analyses of PALSAR and Landsat images.

Abstract: Built-up area supports human settlements and activities, and its spatial distribution and temporal dynamics have significant impacts on ecosystem services and global environment change. To date, most of urban remote sensing has generated the maps of impervious surfaces, and limited effort has been made to explicitly identify the area, location and density of built-up in the complex and fragmented landscapes based on the freely available datasets. In this study, we took the lower Yangtze River Delta (Landsat Path/Row: 118/038), China, where extensive urbanization and industrialization have occurred, as a case study site. We analyzed the structure and optical features of typical land cover types from (1) the HH and HV gamma-naught imagery from the Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), and (2) time series Landsat imagery. We proposed a pixel- and rule-based decision tree approach to identify and map built-up area at 30-m resolution from 2007 to 2010, using PALSAR HH gamma-naught and Landsat annual maximum Normalized Difference Vegetation Index (NDVImax). The accuracy assessment showed that the resultant annual maps of built-up had relatively high user (87–93%) and producer accuracies (91–95%) from 2007 to 2010. The built-up area was 2805 km2 in 2010, about 16% of the total land area of the study site. The annual maps of built-up in 2007–2010 show relatively small changes in the urban core regions, but large outward expansion along the peri-urban regions. The average annual increase of built-up area s was about 80 km2 per year from 2007 to 2010. Our annual maps of built-up in the lower Yangtze River Delta clearly complement the existing maps of impervious surfaces in the region. This study provides a promising new approach to identify and map built-up area, which is critical to investigate the interactions between human activities and ecosystem services in urban-rural systems.

Intensifying poultry production systems and the emergence of avian influenza in China: a 'One Health/Ecohealth' epitome.

Abstract: Several kinds of pressure can lead to the emergence of infectious diseases. In the case of zoonoses emerging from livestock, one of the most significant changes that has taken place since the mid twentieth century is what has been termed the “livestock revolution”, whereby the stock of food animals, their productivity and their trade has increased rapidly to feed rising and increasingly wealthy and urbanized populations. Further increases are projected in the future in low and middle-income countries. Using avian influenza as an example, we discuss how the emergence of avian influenza H5N1 and H7N9 in China was linked to rapid intensification of the poultry sector taking place in landscapes rich in wetland agriculture and wild waterfowls habitats, providing an extensive interface with the wild reservoir of avian influenza viruses. Trade networks and live-poultry markets further exacerbated the spread and persistence of avian influenza as well as human exposure. However, as the history of emergence of highly pathogenic avian influenza (HPAI) demonstrates in high-income countries such as the USA, Canada, Australia, the United Kingdom or the Netherlands, this is by no way specific to low and middle-income countries. Many HPAI emergence events took place in countries with generally good biosecurity standards, and the majority of these in regions hosting intensive poultry production systems. Emerging zoonoses are only one of a number of externalities of intensive livestock production systems, alongside antimicrobial consumption, disruption of nutrient cycles and greenhouse gases emissions, with direct or indirect impacts on human health. In parallel, livestock production is essential to nutrition and livelihoods in many low-income countries. Deindustrialization of the most intensive production systems in high-income countries and sustainable intensifications in low-income countries may converge to a situation where the nutritional and livelihood benefits of livestock production would be less overshadowed by its negative impacts on human an ecosystem health.

Different Patterns in Daytime and Nighttime Thermal Effects of Urbanization in Beijing-Tianjin-Hebei Urban Agglomeration

Abstract: Surface urban heat island (SUHI) in the context of urbanization has gained much attention in recent decades; however, the seasonal variations of SUHI and their drivers are still not well documented. In this study, the Beijing-Tianjin-Hebei (BTH) urban agglomeration, one of the most typical areas experiencing drastic urbanization in China, was selected to study the SUHI intensity (SUHII) based on remotely sensed land surface temperature (LST) data. Pure and unchanged urban and rural pixels from 2000 to 2010 were chosen to avoid non-concurrency between land cover data and LST data and to estimate daytime and nighttime thermal effects of urbanization. Different patterns of the seasonal variations were found in daytime and nighttime SUHIIs. Specifically, the daytime SUHII in summer (4 °C) was more evident than in other seasons while a cold island phenomenon was found in winter; the nighttime SUHII was always positive and higher than the daytime one in all the seasons except summer. Moreover, we found the highest daytime SUHII in August, which is the growing peak stage of summer maize, while nighttime SUHII showed a trough in the same month. Seasonal variations of daytime SUHII showed higher significant correlations with the seasonal variations of ∆LAI (leaf area index) (R2 = 0.81, r = −0.90) compared with ∆albedo (R2 = 0.61, r = −0.78) and background daytime LST (R2 = 0.69, r = 0.83); moreover, agricultural practices (double-cropping system) played an important role in the seasonal variations of daytime SUHII. Seasonal variations of the nighttime SUHII did not show significant correlations with either of seasonal variations of ∆LAI, ∆albedo, and background nighttime LST, which implies different mechanisms in nighttime SUHII variation needing future studies.

Temporal consistency between gross primary production and solar-induced chlorophyll fluorescence in the ten most populous megacity areas over years.

Abstract: The gross primary production (GPP) of vegetation in urban areas plays an important role in the study of urban ecology. It is difficult however, to accurately estimate GPP in urban areas, mostly due to the complexity of impervious land surfaces, buildings, vegetation, and management. Recently, we used the Vegetation Photosynthesis Model (VPM), climate data, and satellite images to estimate the GPP of terrestrial ecosystems including urban areas. Here, we report VPM-based GPP (GPPvpm) estimates for the world’s ten most populous megacities during 2000–2014. The seasonal dynamics of GPPvpm during 2007–2014 in the ten megacities track well that of the solar-induced chlorophyll fluorescence (SIF) data from GOME-2 at 0.5° × 0.5° resolution. Annual GPPvpm during 2000–2014 also shows substantial variation among the ten megacities, and year-to-year trends show increases, no change, and decreases. Urban expansion and vegetation collectively impact GPP variations in these megacities. The results of this study demonstrate the potential of a satellite-based vegetation photosynthesis model for diagnostic studies of GPP and the terrestrial carbon cycle in urban areas.

Comparison of Pixel- and Object-Based Approaches in Phenology-Based Rubber Plantation Mapping in Fragmented Landscapes

Abstract: The increasing expansion of rubber plantations throughout East and Southeast Asia urgently requires improved methods for effective mapping and monitoring. The phenological information from rubber plantations was found effective in rubber mapping. Previous studies have mostly applied rule-pixel-based phenology approaches for rubber plantations mapping, which might result in broken patches in fragmented landscapes. This study introduces a new paradigm by combining phenology information with object-based classification to map fragmented patches of rubber plantations in Xishuangbanna. This research first delineated the time windows of the defoliation and foliation phases of rubber plantations by acquiring the temporal profile and phenological features of rubber plantations and natural forests through the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data. To investigate the ability of finer resolution images at capturing the temporal profile or phenological information, 30 m resolution Landsat image data were used to capture the temporal profile, and a phenology algorithm to separate rubber plantations and natural forests was then defined. The derived phenology algorithm was used by both the object-based and pixel-based classification to investigate whether the object-based approach could improve the mapping accuracy. Whether adding the phenology information to the object-based classification could improve rubber plantation mapping accuracy in mountainous Xishuangbanna was also investigated. This resulted in three approaches: rule-pixel-based phenology, rule-object-based phenology, and nearest-neighbor-object-based phenology. The results showed that the rule-object-based phenology approaches (with overall accuracy 77.5% and Kappa Coefficients of 0.66) and nearest-neighbor-object-based phenology approach (91.0% and 0.86) achieved a higher accuracy than that of the rule-pixel-based phenology approach (72.7% and 0.59). The results proved that (1) object-based approaches could improve the accuracy of rubber plantation mapping compared to the pixel-based approach and (2) incorporating the phenological information from vegetation improved the overall accuracy of the thematic map.

Spatial-temporal consistency between gross primary productivity and solar-induced chlorophyll fluorescence of vegetation in China during 2007-2014

Abstract: Accurately estimating spatial-temporal patterns of gross primary production (GPP) is important for the global carbon cycle. Satellite-based light use efficiency (LUE) models are regarded as an efficient tool in simulating spatial-temporal dynamics of GPP. However, the accuracy assessment of GPP simulations from LUE models at both spatial and temporal scales remains a challenge. In this study, we simulated GPP of vegetation in China during 2007-2014 using a LUE model (Vegetation Photosynthesis Model, VPM) based on MODIS (moderate-resolution imaging spectroradiometer) images with 8-day temporal and 500-m spatial resolutions and NCEP (National Center for Environmental Prediction) climate data. Global Ozone Monitoring Instrument 2 (GOME-2) solar-induced chlorophyll fluorescence (SIF) data were used to compare with VPM simulated GPP (GPPVPM) temporally and spatially using linear correlation analysis. Significant positive linear correlations exist between monthly GPPVPM and SIF data over a single year (2010) and multiple years (2007-2014) in most areas of China. GPPVPM is also significantly positive correlated with GOME-2 SIF (R2 > 0.43) spatially for seasonal scales. However, poor consistency was detected between GPPVPM and SIF data at yearly scale. GPP dynamic trends have high spatial-temporal variation in China during 2007-2014. Temperature, leaf area index (LAI), and precipitation are the most important factors influence GPPVPM in the regions of East Qinghai-Tibet Plateau, Loss Plateau, and Southwestern China, respectively. The results of this study indicate that GPPVPM is temporally and spatially in line with GOME-2 SIF data, and space-borne SIF data have great potential for evaluating LUE-based GPP models.

Assessing agricultural drought in summer over Oklahoma Mesonet sites using the water-related vegetation index from MODIS.

Abstract: Agricultural drought, a common phenomenon in most parts of the world, is one of the most challenging natural hazards to monitor effectively. Land surface water index (LSWI), calculated as a normalized ratio between near infrared (NIR) and short-wave infrared (SWIR), is sensitive to vegetation and soil water content. This study examined the potential of a LSWI-based, drought-monitoring algorithm to assess summer drought over 113 Oklahoma Mesonet stations comprising various land cover and soil types in Oklahoma. Drought duration in a year was determined by the number of days with LSWI 80 % (eastern Oklahoma) across regions. Our results illustrated that drought intensity thresholds can be established by counting DNLSWI (in days) and used as a simple complementary tool in several drought applications for semi-arid and semi-humid regions of Oklahoma. However, larger discrepancies between USDM and the LSWI-based algorithm in arid regions of western Oklahoma suggest the requirement of further adjustment in the algorithm for its application in arid regions.

Application of the space-for-time substitution method in validating long-term biomass predictions of a forest landscape model

Abstract: Validation of the long-term biomass predictions of forest landscape models (FLMs) has always been a challenging task. Using the space-for-time substitution method, forest biomass curves over stand age were generated from a forest survey dataset (FSD) in the Lesser Khingan Mountains area (LKM), Northeastern China and compared with long-term biomass predictions of LANDIS-II model. The results showed that mean forest age and mean biomass of the LKM in 2000 were 51.6 years and 84.2 Mg ha−1, respectively. Significant linear correlations were found between FSD derived biomass and simulated biomass in the aggradation phase for the entire LKM and most subregions. However, a considerable difference in the mean maximum biomass (53.45 Mg ha−1) existed between from FSD and simulation during the post-aggradation phase. The space-for-time substitution method has potential in validating time series biomass predictions of FLMs in aggradation phase when only limited forest inventory data is available.

H7N9 and H5N1 avian influenza suitability models for China: accounting for new poultry and live-poultry markets distribution data

Abstract: In the last two decades, two important avian influenza viruses infecting humans emerged in China, the highly pathogenic avian influenza (HPAI) H5N1 virus in the late nineties, and the low pathogenic avian influenza (LPAI) H7N9 virus in 2013. China is home to the largest population of chickens (4.83 billion) and ducks (0.694 billion), representing, respectively 23.1 and 58.6% of the 2013 world stock, with a significant part of poultry sold through live-poultry markets potentially contributing to the spread of avian influenza viruses. Previous models have looked at factors associated with HPAI H5N1 in poultry and LPAI H7N9 in markets. However, these have not been studied and compared with a consistent set of predictor variables. Significant progress was recently made in the collection of poultry census and live-poultry market data, which are key potential factors in the distribution of both diseases. Here we compiled and reprocessed a new set of poultry census data and used these to analyse HPAI H5N1 and LPAI H7N9 distributions with boosted regression trees models. We found a limited impact of the improved poultry layers compared to models based on previous poultry census data, and a positive and previously unreported association between HPAI H5N1 outbreaks and the density of live-poultry markets. In addition, the models fitted for the HPAI H5N1 and LPAI H7N9 viruses predict a high risk of disease presence for the area around Shanghai and Hong Kong. The main difference in prediction between the two viruses concerned the suitability of HPAI H5N1 in north-China around the Yellow sea (outlined with Tianjin, Beijing, and Shenyang city) where LPAI H7N9 has not spread intensely.

Long-term analysis of the asynchronicity between temperature and precipitation maxima in the United States Great Plains

Abstract: Agriculture is a critical industry to the economy of the Great Plains (GP) region of North America and sensitive to change in weather and climate. Thus, improved knowledge of meteorological and climatological conditions during the growing season and associated variability across spatial and temporal scales is important. A distinct climate feature in the GP is the asynchronicity (AS) between the timing of temperature and precipitation maxima. This study investigated a long-term observational data set to quantify the AS and to address the impacts of climate variability and change. Global Historical Climate Network Daily (GHCN-Daily) data were utilized for this study; 352 GHCN-Daily stations were identified based on specific criteria and the dates of the precipitation and temperature maxima for each year were identified at daily and weekly intervals. An asynchronous difference index (ADI) was computed by determining the difference between these dates averaged over each decade. Analysis of daily and weekly ADI revealed two physically distinct regimes of ADI (positive and negative), with comparable shifts in the timing of both the maximum of precipitation and temperature over all six states within the GP examined when comparing the two different regimes. Time series analysis of decadal average ADI yielded moderate shifts (∼5 to 10 days from linear regression analysis) in ADI in several states with increased variability occurring over much of the study region.

Could changes in the agricultural landscape of northeastern China have influenced the long-distance transmission of highly pathogenic avian influenza H5Nx viruses?

Abstract: In the last few years, several reassortant subtypes of highly pathogenic avian influenza viruses (HPAI H5Nx) have emerged in East Asia. These new viruses, mostly of subtype H5N1, H5N2, H5N6, and H5N8 belonging to clade 2.3.4.4, have been found in several Asian countries and have caused outbreaks in poultry in China, South Korea, and Vietnam. HPAI H5Nx also have spread over considerable distances with the introduction of viruses belonging to the same 2.3.4.4 clade in the U.S. (2014-2015) and in Europe (2014-2015 and 2016-2017). In this paper, we examine the emergence and spread of these new viruses in Asia in relation to published datasets on HPAI H5Nx distribution, movement of migratory waterfowl, avian influenza risk models, and land-use change analyses. More specifically, we show that between 2000 and 2015, vast areas of northeast China have been newly planted with rice paddy fields (3.21 million ha in Heilongjiang, Jilin, and Liaoning) in areas connected to other parts of Asia through migratory pathways of wild waterfowl. We hypothesize that recent land use changes in northeast China have affected the spatial distribution of wild waterfowl, their stopover areas, and the wild-domestic interface, thereby altering transmission dynamics of avian influenza viruses across flyways. Detailed studies of the habitat use by wild migratory birds, of the extent of the wild-domestic interface, and of the circulation of avian influenza viruses in those new planted areas may help to shed more light on this hypothesis, and on the possible impact of those changes on the long-distance patterns of avian influenza transmission.

The impact of surveillance and control on highly pathogenic avian influenza outbreaks in poultry in Dhaka division, Bangladesh

Abstract: In Bangladesh the poultry industry is an economically and socially important sector, but is persistently threatened by the effects of H5N1 highly pathogenic avian influenza. Thus, identifying the optimal control policy in response to an emerging disease outbreak is a key challenge for policy-makers. To inform this aim, a common approach is to carry out simulation studies comparing plausible strategies, while accounting for known capacity restrictions. In this study we perform simulations of a previously developed H5N1 influenza transmission model framework, fitted to two separate historical outbreaks, to assess specific control objectives related to the burden or duration of H5N1 outbreaks among poultry farms in the Dhaka division in Bangladesh. In particular we explore the optimal implementation of ring culling, ring vaccination and active surveillance measures when presuming disease transmission predominately occurs from premises-to-premises, versus a setting requiring the inclusion of external factors. Additionally, we determine the sensitivity of the management actions under consideration to differing levels of capacity constraints and outbreaks with disparate transmission dynamics. While we find that reactive culling and vaccination control policies should pay close attention to these factors to ensure intervention targeting is optimised, targeted proactive active surveillance schemes appear to significantly outperform reactive surveillance procedures in all instances. Our findings may advise the type of control measure, plus its severity, that should be applied in the event of a re-emergent outbreak of H5N1 amongst poultry in the Dhaka division of Bangladesh.