Spatiotemporal analysis of vegetation variability and its relationship with climate change in China

This paper investigated spatiotemporal dynamic pattern of vegetation, climate factor, and their complex relationships from seasonal to inter-annual scale in China during the period 1982–1998 through wavelet transform method based on GIMMS data-sets. First, most vegetation canopies demonstrated obvious seasonality, increasing with latitudinal gradient. Second, obvious dynamic trends were observed in both vegetation and climate change, especially the positive trends. Over 70% areas were observed with obvious vegetation greening up, with vegetation degradation principally in the Pearl River Delta, Yangtze River Delta, and desert. Overall warming trend was observed across the whole country (\u3e98% area), stronger in Northern China. Although over half of area (58.2%) obtained increasing rainfall trend, around a quarter of area (24.5%), especially the Central China and most northern portion of China, exhibited significantly negative rainfall trend. Third, significantly positive normalized difference vegetation index (NDVI)–climate relationship was generally observed on the de-noised time series in most vegetated regions, corresponding to their synchronous stronger seasonal pattern. Finally, at inter-annual level, the NDVI–climate relationship differed with climatic regions and their long-term trends: in humid regions, positive coefficients were observed except in regions with vegetation degradation; in arid, semiarid, and semihumid regions, positive relationships would be examined on the condition that increasing rainfall could compensate the increasing water requirement along with increasing temperature. This study provided valuable insights into the long-term vegetation–climate relationship in China with consideration of their spatiotemporal variability and overall trend in the global change process

An environmental assessment and risk map of Ascaris lumbricoides and Necator americanus distributions in Manufahi District, Timor-Leste

Background In Timor-Leste there have been intermittent and ineffective soil-transmitted helminth (STH) deworming programs since 2004. In a resource-constrained setting, having information on the geographic distribution of STH can aid in prioritising high risk communities for intervention. This study aimed to quantify the environmental risk factors for STH infection and to produce a risk map of STH in Manufahi district, Timor-Leste. Methodology/Principal findings Georeferenced cross-sectional data and stool samples were obtained from 2,194 participants in 606 households in 24 villages in the Manufahi District as part of cross sectional surveys done in the context of the “WASH for Worms” randomised controlled trial. Infection status was determined for Ascaris lumbricoides and Necator americanus using real-time quantitative polymerase chain reaction. Baseline infection data were linked to environmental data obtained for each household. Univariable and multivariable multilevel mixed-effects logistic regression analysis with random effects at the village and household level were conducted, with all models adjusted for age and sex. For A. lumbricoides, being a school-aged child increased the odds of infection, whilst higher temperatures in the coolest quarter of the year, alkaline soils, clay loam/loam soils and woody savannas around households were associated with decreased infection odds. For N. americanus, greater precipitation in the driest month, higher average enhanced vegetation index, age and sandy loam soils increased infection odds, whereas being female and living at higher elevations decreased the odds of infection. Predictive risk maps generated for Manufahi based upon these final models highlight the high predicted risk of N. americanus infection across the district and the more focal nature of A. lumbricoides infection. The predicted risk of any STH infection is high across the entire district. Conclusions/Significance The widespread predicted risk of any STH infection in 6 to 18 year olds provides strong evidence to support strategies for control across the entire geographical area. As few studies include soil texture and pH in their analysis, this study adds to a growing body of evidence suggesting these factors influence STH infection distribution. This study also further supports that A. lumbricoides prefers acidic soils, highlighting a potential relatively unexplored avenue for control

Lower Atmosphere Meteorology

The Atmosphere Special Issue “Lower Atmosphere Meteorology” deals with the meteorological processes that occur in the layer of the atmosphere close to the surface. The interaction between the biosphere and the atmosphere is made through the lower layer and can greatly influence living beings and materials. The analysis of the meteorological parameters provides a better understanding of processes within the lower atmosphere and involved in air pollution, climate, and weather. The mixed layer height, the wind speed, and the air parcel trajectory have a relevant interest due to their marked impact on population and energy production. The research also comprises aerosols, clouds, and precipitation, analysing their spatiotemporal variations. This issue addresses features of gases in the atmosphere and anthropogenic greenhouse emission estimates, which are also conditioned by the lower atmosphere meteorology

Mapping the potential distribution of major tick species in China

Ticks are known as the vectors of various zoonotic diseases such as Lyme borreliosis and tick-borne encephalitis. Though their occurrences are increasingly reported in some parts of China, our understanding of the pattern and determinants of ticks’ potential distribution over the country remain limited. In this study, we took advantage of the recently compiled spatial dataset of distribution and diversity of ticks in China, analyzed the environmental determinants of ten frequently reported tick species and mapped the spatial distribution of these species over the country using the MaxEnt model. We found that presence of urban fabric, cropland, and forest in a place are key determents of tick occurrence, suggesting ticks were likely inhabited close to where people live. Besides, precipitation in the driest month was found to have a relatively high contribution in mapping tick distribution. The model projected that theses ticks could be widely distributed in the Northwest, Central North, Northeast, and South China. Our results added new evidence on the potential distribution of a variety of major tick species in China and pinpointed areas with a high potential risk of tick bites and tick-borne diseases for raising public health awareness and prevention response

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools

Spatial-temporal dynamics of China's terrestrial biodiversity: A dynamic habitat index diagnostic

Biodiversity in China is analyzed based on the components of the Dynamic Habitat Index (DHI). First, observed field survey based spatial patterns of species richness including threatened species are presented to test their linear relationship with remote sensing based DHI (2001-2010 MODIS). Areas with a high cumulative DHI component are associated with relatively high species richness, and threatened species richness increases in regions with frequently varying levels of the cumulative DHI component. The analysis of geographical and statistical distributions yields the following results on interdependence, polarization and change detection: (1) The decadal mean Cumulative Annual Productivity (DHI-cum 4) in Southeast China are in a stable (positive) relation to the Minimum Annual Apparent Cover (DHI-min) and is positively (negatively) related to the Seasonal Variation of Greenness (DHI-sea); (2) The decadal tendencies show bimodal frequency distributions aligned near DHI-min~0.05 and DHI-sea~0.5 which separated by zero slopes; that is, regions with both small DHI-min and DHI-sea are becoming smaller and vice versa; (3) The decadal tendencies identify regions of land-cover change (as revealed in previous research). That is, the relation of strong and significant tendencies of the three DHI components with climatic or anthropogenic induced changes provides useful information for conservation planning. These results suggest that the spatial-temporal dynamics of China's terrestrial species and threatened species richness needs to be monitored by first and second moments of remote sensing based information of the DHI. © 2016 by the authors

Bayesian geostatistical and mathematical models to assess the geographical distribution of neglected tropical diseases

Neglected tropical diseases (NTDs) are a group of communicable diseases affecting more than one billion of the world’s poorest population. Soil-transmitted helminth infections, schistosomiasis, and foodborne trematodiasis are among the most important NTDs. Soil-transmitted helminth infections are caused by a group of parasite nematode worms (i.e., Ascaris lumbricoides, Trichuris trichiura, and hookworm) through contact with parasite eggs or larvae which thrive in warm and moist soil. They are widely endemic in the tropics and sub-tropics and ranked on the top among all NTDs burden, contributing to the global disease burden with 5.2 million disability-adjusted life years (DALYs). Schistosomiasis is caused by trematode parasites of the genus Schistosoma. It is the second highest in terms of NTD burden and responsible for around 3.3 million DALYs worldwide. More than 90% of schistosomiasis cases occur in Africa. Clonorchiasis is one of the most important foodborne trematodiasis and it is caused by infection with the Chinese liver fluke, Clonorchis sinensis. China accounts for around 85% of the global infected people and most cases occur in the southern and the northeastern parts of the country. For all the three diseases, preventive chemotherapy is advocated by WHO as a key strategy for morbidity control. Furthermore, integrated approaches are highly recommended to achieve sustainable control and elimination. Such approaches may include preventive chemotherapy in combination with improvement of water, sanitation, and hygiene, as well as better information, education, and communication. To implement control strategies cost-effectively, high-resolution maps depicting the geographical distribution of disease risk are important. These maps provide useful information for spatial targeting of control measures and for long-term monitoring and surveillance. Geostatistical modeling is the most rigorous inferential approach for high-resolution risk mapping of NTDs. It is a data-driven approach, which relates georeferenced disease data (usually point-referenced) with potential predictors (e.g., environmental and socioeconomic factors) that are considered important for disease transmission. Location-specific random effects can explain geographical variation in the data, assuming that neighboring areas have similar infection status due to common disease exposures they receive. Geostatistical models are highly parameterized, however Bayesian model formulations provide a flexible inferential framework and powerful computational tools such as Markov chain Monte Carlo (MCMC) simulation or approximations (e.g., integrated nested Laplace approximation (INLA)) are applied for model fit. A good coverage and a fine amount of disease data are necessary to capture the spatial heterogeneity of the infection risk. Due to lack of large surveys covering the whole study region, this PhD thesis is based on historical survey data that are compiled via bibliometric searches. Publications however are either report the survey data as point-referenced (with geographical information at the survey location) or as areal, aggregated over several locations within an administrative level (e.g., county or district). The areal data can provide useful information especially when the spatial coverage of point-referenced data is low. Geostatistical model for jointly analysing point-level and areal survey data are not available. Furthermore, historical data are generated from studies with different designs between locations, including different population age-groups. Geostatistical models that align survey data across locations to a common age group do not exist in the field of NTDs. Ignoring the age-heterogeneity of the data can lead to biased estimation because models cannot distinguish whether risk differences between locations is due to differences in age or to exposures. Mathematical models can be used to age-align the surveys, but there is no model formulation allowing changes of the shape of the age-prevalence curve over space as a result of the varying endemicity. The overall goal of the thesis is to develop Bayesian geostatistical and mathematical models for analysing georeferenced NTD survey data and to provide tools and knowledge for disease control and prevention. In Chapter 2 surveys pertaining to soil-transmitted helminth infections in People’s Republic of China (P.R. China) were compiled. Bayesian geostatistical models were developed and used to estimate the disease risk throughout the country at high spatial resolution. Advanced Bayesian variable selection methods were employed to identify the most important predictors. Results indicate that the prevalence of soil-transmitted helminth infections in P.R. China considerably decreased from 2005 onwards. Yet, some 144 million people were estimated to be infected in 2010. High prevalence (>20%) was predicted in large areas of Guizhou and the southern part of Hubei and Sichuan provinces for Ascaris lumbricoides infection, in large areas of Hainan, the eastern part of Sichuan, and the southern part of Yunnan provinces for hookworm infection, as well as in a few small areas of south P.R. China for Trichuris trichiura infection. In Chapter 3 a systematic review was carried out to identify prevalence surveys to soil-transmitted helminth infections in South Asia. Bayesian geostatistical models were applied to identify important environmental and socioeconomic predictors, and to estimate infection risk at high spatial resolution across the study region. Results show that 397 million of South Asia population was infected with at least one species of soil-transmitted helminths in 2015. A. lumbricoides was the most common infection species. Moderate to high prevalence (>20%) of any soil-transmitted helminth infection was predicted in the northeastern part and some northern areas of the study region as well as the southern coastal-line areas of India. The annual treatment needs for the school-aged population requiring preventive chemotherapy was estimated at 187 million doses. The study highlights the need for up-to-date surveys to accurately evaluate the disease burden in the region. In Chapter 4 georeferenced survey data of C. sinensis infection were obtained via a systematic review and additional data were provided by the National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention. Bayesian geostatistical models were applied to quantify the relation between infection risk and important predictors, and to predict the risk of infection across P.R. China at high spatial resolution. The results show an increasing risk of C. sinensis infection over time, particularly from 2005 onwards, which urges the Chinese government to pay more attention on the public health importance of the diseases. Highly endemic areas (>20%) were concentrated in southern and northeastern parts of the country. The provinces with the highest risk of infection and the largest number of infected people were Guangdong, Guangxi and Heilongjiang. In Chapter 5 a systematic review was conducted to identify relevant surveys pertaining to prevalence of Schistosoma infection in sub-Saharan Africa. Bayesian geostatistical meta-analysis and rigorous variable selection were used to obtain up-to-date risk estimates of schistosomiasis at high spatial resolution, based on environmental and socioeconomic predictors. The literature search identified Schistosoma haematobium and Schistosoma mansoni surveys at 9,318 and 9,140 unique locations, respectively. Results show a decreased infection risk from 2000 onwards, yet suggesting that 163 million Africans were infected in 2012. Mozambique had the highest prevalence of Schistosoma infection among 44 countries of sub-Saharan Africa. Annualised treatment needs with praziquantel were estimated at 123 million doses for school-aged children and 247 million for the entire population. In Chapter 6 a Bayesian geostatistical modeling approach was developed to analyse jointly areal and point-referenced survey data. We assumed that the point-referenced data arise from a binomial distribution and that the aggregated area data follow a Poisson binomial distribution which was approximated by a two parameter shifted binomial distribution. Results from extensive simulations shows that our proposed model has better predictive ability and improves parameter estimation compared to models that treat area data as points, located at the centroid of the areas. We applied the new model to obtain high spatial resolution estimates of the infection risk of clonorchiasis in an endemic region of P.R. China. In Chapter 7 we integrated geostatistical and mathematical transmission models of schistosomiasis within a single model formulation to analyse age-heterogenous S. mansoni data from Côte d’Ivoire. A series of age-specific risk maps of S. mansoni infection in Côte d’Ivoire were produced at high geographical resolution, which allow us to identify the most important age groups of the population to treat at a given place. We predicted that the infection risk reached the peak at younger ages in high risk areas and at older ages in low risk areas. Moreover, a more rapid decline rate of infection risk was observed at older ages in high risk areas compared to that in moderate and low risk ones. In summary, this PhD thesis contributes to the fields of spatial statistics and of epidemiology of NTDs with (i) statistical methodology for modeling spatially-structured disease data, having heterogeneous geographical support (i.e., georeferenced at point or area level) across the study region and they are collected over different age groups between locations, (ii) applications on soil transmitted helminth infections, schistosomiasis, and clonorchiasis in sub-Saharan Africa, South Asia, and P.R. China, to obtain spatially explicit estimates of disease risk, number of infected people, and annual treatment needs for preventive chemotherapy at different administrative levels, and (iii) large amount of geo-referenced data on NTD surveys conducted at over 10,750 unique locations that are available via the open access Global Neglected Tropical Diseases Database (GNTD). The innovative statistical methodology for analysing historical survey data, heterogeneous in space can be readily applied to other disease survey data. The up-to-date, model-based, high-resolution risk maps and estimates of treatment needs provide useful tools and information for guiding disease control and interventions

Time series analysis of high resolution remote sensing data to assess degradation of vegetation cover of the island of Socotra (Yemen)

The island of Socotra has long been in geographical isolation, hence nearly 30% of the plant species are believed to be endemic to the island. Until the end of 20th century there was only very little and incomplete information and literature about the vegetation on the island. This isolation broke down in 1990 with the country unification in which then the island received much attention. Subsequently the scientific knowledge of the local flora slowly increased, but many of plant species are now reported to be confined into small populations, hence being particularly vulnerable to habitat loss, overgrazing, as well as urban expansion. 1. The overall objective of this research attempted to assess and examine the trends of vegetation changes since 1972 to 2010 with the use of Landsat MSS, TM and ETM+ images and to investigate the related driving factors, such as rainfall, grazing pressure changes and underlying spatial variability of the landscape. This is to answer the overall question: Is there a trend in biomass, cover and species composition on Socotra Island over the last 40 years? If so, is that trend associated with the rainfall patterns? What are the drivers behind the vegetation change? And then how can we define changes in patterns or changes in this study area? 2. From a methodological point of view, our approach of systematically using remote sensing technology data proved scientifically an applicable tool to improve our understanding of the spatial complexity and heterogeneity of the vegetation cover as well as to provide a conceptual method with specific data for monitoring the changes over this time period. Our data obtained from these different Landsat sensors during the study period were - after many sophisticated processing steps - essentially able to provide time series information for Normalized Difference Vegetation Index (NDVI) data and to assess the long term trend in vegetation cover in the island. 3. Moreover, our approach combining supervised maximum-likelihood and unsupervised classification with the pre- and the post-classification approaches besides the knowledge based classification was table to provide sufficient results to distinguish and to map nine (9) terrestrial vegetation cover classes. The overall accuracy (compared with ground truth data) was about 91%, 77%, 70% and 72% for the images 2005, 1994, 1984 and 1972 respectively. Consecutively, the GIS analysis allowed estimates of highly valuable information as absolute areas and relative coverage of particular vegetation classes over the island with their spatial distribution and also their ecological requirements. Analysis of climatic conditions and NDVI 4. As a results of the complex topography of the study area and the wide climate range, with the guidance of prior knowledge of functional relationships between site parameters, ecosystem and the specific form of biological production, our work resulted in a division of the entire area into six variously sized ecosystem units, which were enough to properly depict the spatial heterogeneity of the rainfall and vegetation and to assist reflecting the influence and reaction between environmental parameters as well as it might have significance both for development of resources and for conservation of environment

Remote Sensing of Savannas and Woodlands

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome