Schistosomiasis control in China : strategy of control and rapid assessment of schistosomiasis risk by remote sensing (RS)and geographic information system (GIS)

Human schistosomiasis remains one of the most prevalent parasitic infections in the tropics and subtropics. The disease currently is endemic in 76 countries and territories and continues to be a major public health concern, especially in the developing world. It is estimated that 650 million people are at risk of infection. Among the 200 million people actually infected, 120 million are symptomatic and 20 million suffer severe disease. Although morbidity control – in line with recommendations put forth by the World Health Organization – has been carried out in China for more than 20 years, it is estimated that 90 million people still live in areas where they are at risk of infection, and 820,000 people are infected with the parasite, i.e. Schistosoma japonicum. The estimated area of intermediate host snail habitats comprise 3,436 km2, concentrated in the 5 lake regions along the Yangtze River that include the provinces of Anhui, Jiangsu, Jiangxi, Hubei and Hunan. The marshlands of the Poyang Lake region represent some of the strongholds for the transmission of S. japonicum. In these settings, for example, the percentages of acute cases and intermediate host snail habitats represent 79.5% and 96.4%, respectively. With the World Bank Loan Project (WBLP) to control schistosomiasis in China, the overall prevalence of S. japonicum was significantly reduced, but in highly endemic areas the re-infection rates are high. In the first part of the present thesis, I summarize the 50-year history of China’s experience and expertise in schistosomiasis control. Particular emphasis is placed on morbidity control and achievements made by the WBLP carried out between 1992 and 2001. Reviewing this body of literature reveals that morbidity control of schistosomiasis in China has been successful, and hence this strategy will continue to form the backbone of protecting people’s health. However, total expenditures have been considerable, and with the termination of the WBLP there is concern that schistosomiasis might re-emerge. In the second part of this thesis, I describe the successful development of a novel compound model to identify the habitats of Oncomelania hupensis, the intermediate host snail of S. japonicum, and hence the identification of high-risk areas of disease transmission. There are three findings that warrant particular notion. First, visual land use classification on multi-temporal Landsat images was performed for preliminary prediction of O. hupensis habitats. Second, extraction of the normalized difference vegetation index and the tasseled cap transformation greenness index were used for improved snail habitat prediction. Third, buffer zones with distances of 600 and 1,200 m were made around the predicted snail habitats to differentiate between high (>15%), moderate (3-15%) and low risk of S. japonicum infection prevalence (< 3%). Preliminary validation of the compound model against ground-based snail surveys in the Poyang Lake region revealed that the model had an excellent predictive ability. The model therefore holds promise for rapid and inexpensive identification of high-risk areas, and can guide subsequent control interventions, such as whether mass or selective chemotherapy should be employed. The model can also be used for diseases surveillance in general and the monitoring of ecological transformations on the transmission dynamics of S. japonicum, for example in the Three Gorges Dam area

Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions on how to capitalize on state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world

Energy-Water Balance and Ecosystem Response to Climate Change in Southwest China

It is important to highlight energy-water balance and ecosystem response to climate changes. The change of water-energy balance and ecosystem due to climate change will affect the regional ecological and human living significantly, especially in Southwest China which is an ecologically fragile area. This chapter presents the retrieval methodology of parameters (reconstruction of vegetation index, land cover semi-automatic classification, a time series reconstruction of land surface temperature based on Kalman filter and precipitation interpolation based on thin plate smoothing splines), time-series analysis methodology (land cover change, vegetation succession and drought index) and correlate analysis methodology (correlation coefficient and principal component analysis). Then, based on the above method, remote sensing data were integrated, a time series analysis on a 30-year data was used to illustrate the water-energy balance and ecosystem variability in Southwest China. The result showed that energy-water balance and ecosystem (ecosystem structures, vegetation and droughts) have severe response to climate change

Evaluating the impact of highway construction projects on landscape ecological risks in high altitude plateaus

[EN] In China and other countries, many highway projects are built in extensive and high-altitude flat areas called plateaus. However, research on how the materialisation of these projects produce a series of ecological risks in the landscape is very limited. In this research, a landscape ecological risk analysis model for high-altitude plateaus is proposed. This model is based on the pattern of land uses of the surrounding area. Our study includes buffer analysis, spatial analysis, and geostatistical analysis. We apply the model to the Qumei to Gangba highway, a highway section located in the southeast city of Shigatse at the Chinese Tibet autonomous region. Through global and local spatial autocorrelation analysis, the spatial clustering distribution of ecological risks is also explored. Overall, our study reveals the spatial heterogeneity of ecological risks and how to better mitigate them. According to a comparison of the risk changes in two stages (before and after the highway construction), the impact of highway construction on the ecological environment can be comprehensively quantified. This research will be of interest to construction practitioners seeking to minimize the impact of highway construction projects on the ecological environment. It will also inform future empirical studies in the area of environmental engineering with potential affection to the landscape in high-altitude plateaus.This research is supported by the Branch of China Road and Bridge Corporation (Cambodia) Technology Development Project (No.2020-zlkj-04); National Social Science Fund Projects (No.20BJY010); National Social Science Fund Post-financing Projects (No.19FJYB017); Sichuan-Tibet Railway Major Fundamental Science Problems Special Fund (No.71942006); Qinghai Natural Science Foundation (No. 2020-JY-736); List of Key Science and Technology Projects in China's Transportation Industry in 2018-International Science and Technology Cooperation Project (Nos. 2018-GH-006 and 2019-MS5-100); Emerging Engineering Education Research and Practice Project of Ministry of Education of China (No. E-GKRWJC20202914); Shaanxi Social Science Fund (No. 2017S004); Xi'an Construction Science and Technology Planning Project (Nos. SZJJ201915 and SZJJ201916); Shaanxi Province Higher Education Teaching Reform Project (No. 19BZ016); Fundamental Research for Funds for the Central Universities (Humanities and Social Sciences), Chang'an University (Nos. 300102239616, 300102281669 and 300102231641).Li, C.; Zhang, J.; Philbin, SP.; Yang, X.; Dong, Z.; Hong, J.; Ballesteros-Pérez, P. (2022). Evaluating the impact of highway construction projects on landscape ecological risks in high altitude plateaus. Scientific Reports. 12(1):1-16. https://doi.org/10.1038/s41598-022-08788-811612

Evaluating the impact of highway construction projects on landscape ecological risks in high altitude plateaus

In China and other countries, many highway projects are built in extensive and high-altitude flat areas called plateaus. However, research on how the materialisation of these projects produce a series of ecological risks in the landscape is very limited. In this research, a landscape ecological risk analysis model for high-altitude plateaus is proposed. This model is based on the pattern of land uses of the surrounding area. Our study includes buffer analysis, spatial analysis, and geostatistical analysis. We apply the model to the Qumei to Gangba highway, a highway section located in the southeast city of Shigatse at the Chinese Tibet autonomous region. Through global and local spatial autocorrelation analysis, the spatial clustering distribution of ecological risks is also explored. Overall, our study reveals the spatial heterogeneity of ecological risks and how to better mitigate them. According to a comparison of the risk changes in two stages (before and after the highway construction), the impact of highway construction on the ecological environment can be comprehensively quantified. This research will be of interest to construction practitioners seeking to minimize the impact of highway construction projects on the ecological environment. It will also inform future empirical studies in the area of environmental engineering with potential affection to the landscape in high-altitude plateaus

Flash Flood Risk Analysis Based on Machine Learning Techniques in the Yunnan Province, China

Flash flood, one of the most devastating weather-related hazards in the world, has become more and more frequent in past decades. For the purpose of flood mitigation, it is necessary to understand the distribution of flash flood risk. In this study, artificial intelligence (Least squares support vector machine: LSSVM) and classical canonical method (Logistic regression: LR) are used to assess the flash flood risk in the Yunnan Province based on historical flash flood records and 13 meteorological, topographical, hydrological and anthropological factors. Results indicate that: (1) the LSSVM with Radial basis function (RBF) Kernel works the best (Accuracy = 0.79) and the LR is the worst (Accuracy = 0.75) in testing; (2) flash flood risk distribution identified by the LSSVM in Yunnan province is near normal distribution; (3) the high-risk areas are mainly concentrated in the central and southeastern regions, where with a large curve number; and (4) the impact factors contributing the flash flood risk map from higher to low are: Curve number &gt; Digital elevation &gt; Slope &gt; River density &gt; Flash Flood preventions &gt; Topographic Wetness Index &gt; annual maximum 24 h precipitation &gt; annual maximum 3 h precipitation

An efficient decision support system for flood inundation management using intermittent remote-sensing data

Abstract: Timely acquisition of spatial flood distribution is an essential basis for flood-disaster monitoring and management. Remote-sensing data have been widely used in water-body surveys. However, due to the cloudy weather and complex geomorphic environment, the inability to receive remote-sensing images throughout the day has resulted in some data being missing and unable to provide dynamic and continuous flood inundation process data. To fully and effectively use remote-sensing data, we developed a new decision support system for integrated flood inundation management based on limited and intermittent remote-sensing data. Firstly, we established a new multi-scale water-extraction convolutional neural network named DEU-Net to extract water from remote-sensing images automatically. A specific datasets training method was created for typical region types to separate the water body from the confusing surface features more accurately. Secondly, we built a waterfront contour active tracking model to implicitly describe the flood movement interface. In this way, the flooding process was converted into the numerical solution of the partial differential equation of the boundary function. Space upwind difference format and the time Euler difference format were used to perform the numerical solution. Finally, we established seven indicators that considered regional characteristics and flood-inundation attributes to evaluate flood-disaster losses. The cloud model using the entropy weight method was introduced to account for uncertainties in various parameters. In the end, a decision support system realizing the flood losses risk visualization was developed by using the ArcGIS application programming interface (API). To verify the effectiveness of the model constructed in this paper, we conducted numerical experiments on the model’s performance through comparative experiments based on a laboratory scale and actual scale, respectively. The results were as follows: (1) The DEU-Net method had a better capability to accurately extract various water bodies, such as urban water bodies, open-air ponds, plateau lakes etc., than the other comparison methods. (2) The simulation results of the active tracking model had good temporal and spatial consistency with the image extraction results and actual statistical data compared with the synthetic observation data. (3) The application results showed that the system has high computational efficiency and noticeable visualization effects. The research results may provide a scientific basis for the emergency-response decision-making of flood disasters, especially in data-sparse regions