A spatiotemporal epidemiological investigation of the impact of environmental change on the transmission dynamics of Echinococcus spp. in Ningxia Hui Autonomous Region, China

Background: Human echinococcoses are zoonotic parasitic diseases of major public health importance globally. According to recent estimates, the geographical distribution of echinococcosis is expanding and becoming an emerging and re-emerging problem in several regions of the world. Echinococcosis endemicity is geographically heterogeneous and might be affected by global environmental change over time. The aims of my research were: 1) to assess and quantify the spatiotemporal variation in land cover and climate change in Ningxia Hui Autonomous Region (NHAR); 2) to identify highly endemic areas for human echinococcoses in NHAR, and to determine the environmental covariates that have shaped the local geographical distribution of the disease; 3) to develop spatial statistical models that explain and predict the spatiotemporal variation of human exposure to Echinococcus spp. in a highly endemic county of NHAR; and 4) to analyse associations between the environment and the spatiotemporal variation of human exposure to the parasites and dog infections with Echinococcus granulosus and Echinococcus multilocularis in four echinococcosis-endemic counties of NHAR. Methods: Data on echinococcosis infections and human exposure to E. granulosus and E. multilocularis were obtained from different sources: 1) A hospital-based retrospective survey of human echinococcosis cases in NHAR between 1992 and 2013; 2) three cross-sectional surveys of school children conducted in Xiji County in 2002–2003, 2006–2007 and 2012–2013; and 3) A cross-sectional survey of human exposure and dog infections with E. granulosus and E. multilocularis conducted in Xiji, Haiyuan, Guyuan and Tongxin Counties. Environmental data were derived from high-resolution (30 m) imagery from Landsat 4/5-TM and 8-OLI and meteorological reports provided by the Chinese Academy of Sciences. Image analysis techniques and a Bayesian statistical framework were used to conduct a land cover change detection analyses and to develop regression models that described and quantified climate trends and the environmental factors associated with echinococcosis risk at different spatial scales. Results: The land cover changes observed in NHAR from 1991 to 2015 concurred with the main goals of a national policy on payments for ecosystem services, implemented in the Autonomous Region, in increasing forest and herbaceous vegetation coverages and in regenerating bareland. Statistically significant positive trends were observed in annual, summer and winter temperatures in most of the region, and a small magnitude change was found in annual precipitation, in the same 25-year period. The south of NHAR was identified as a highly endemic area for cystic echinococcosis (CE; caused by E. granulosus) and alveolar echinococcosis (AE; caused by E. multilocularis). Selected environmental covariates explained most of the spatial variation in AE risk, while the risk of CE appeared to be less spatially variable at the township level. The risk of exposure to E. granulosus expanded across Xiji County from 2002–2013, while the risk of exposure to E. multilocularis became more confined in communities located in the south of this highly endemic area. In 2012–2013, the predicted seroprevalences of human exposure to E. granulosus and dog infection with this parasite were characterised by similar geographical patterns across Xiji, Haiyuan, Guyuan and Tongxin Counties. By contrast, the predicted high seroprevalence areas for human exposure and dog infection with E. multilocularis did not coincide spatially. Climate, land cover and landscape fragmentation played a key role in explaining some of the observed spatial variation in the risk of infection with Echinococcus spp. among schoolchildren and dogs in the south of NHAR at the village level. Conclusions: The findings of this research defined populations at a high risk of human exposure to E. granulosus and E. multilocularis in NHAR. The research provides evidence on the potential effects of landscape regeneration projects on the incidence of human echinococcoses due to the associations found between the infections and regenerated land. This information will be essential to track future requirements for scaling up and targeting the control strategies proposed by the National Action Plan for Echinococcosis Control in China and may facilitate the design of future ecosystem management and protection policies and a more effective response to emerging local environmental risks. The predictive models developed as part of this research can also be used to monitor echinococcosis infections and the emergence in Echinococcus spp. transmission in the most affected areas

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

Forest landscapes and global change. New frontiers in management, conservation and restoration. Proceedings of the IUFRO Landscape Ecology Working Group International Conference

This volume contains the contributions of numerous participants at the IUFRO Landscape Ecology Working Group International Conference, which took place in Bragança, Portugal, from 21 to 24 of September 2010. The conference was dedicated to the theme Forest Landscapes and Global Change - New Frontiers in Management, Conservation and Restoration. The 128 papers included in this book follow the structure and topics of the conference. Sections 1 to 8 include papers relative to presentations in 18 thematic oral and two poster sessions. Section 9 is devoted to a wide-range of landscape ecology fields covered in the 12 symposia of the conference. The Proceedings of the IUFRO Landscape Ecology Working Group International Conference register the growth of scientific interest in forest landscape patterns and processes, and the recognition of the role of landscape ecology in the advancement of science and management, particularly within the context of emerging physical, social and political drivers of change, which influence forest systems and the services they provide. We believe that these papers, together with the presentations and debate which took place during the IUFRO Landscape Ecology Working Group International Conference – Bragança 2010, will definitively contribute to the advancement of landscape ecology and science in general. For their additional effort and commitment, we thank all the participants in the conference for leaving this record of their work, thoughts and science

Automated bird counting with deep learning for regional bird distribution mapping

A challenging problem in the field of avian ecology is deriving information on bird population movement trends. This necessitates the regular counting of birds which is usually not an easily-achievable task. A promising attempt towards solving the bird counting problem in a more consistent and fast way is to predict the number of birds in different regions from their photos. For this purpose, we exploit the ability of computers to learn from past data through deep learning which has been a leading sub-field of AI for image understanding. Our data source is a collection of on-ground photos taken during our long run of birding activity. We employ several state-of-the-art generic object-detection algorithms to learn to detect birds, each being a member of one of the 38 identified species, in natural scenes. The experiments revealed that computer-aided counting outperformed the manual counting with respect to both accuracy and time. As a real-world application of image-based bird counting, we prepared the spatial bird order distribution and species diversity maps of Turkey by utilizing the geographic information system (GIS) technology. Our results suggested that deep learning can assist humans in bird monitoring activities and increase citizen scientists’ participation in large-scale bird surveys.No sponso

ENHANCING INVERSE MODELING IN HYDROGEOLOGY WITH MODERN MACHINE LEARNING ALGORITHMS

Inverse estimation of spatially distributed parameter fields plays an important role in many scientific disciplines including hydrogeology, geophysics, earth science, environmental engineering, etc. Classic stochastic sampling approaches such as Markov Chain Monte Carlo (MCMC) and optimization approaches such as geostatistical approach (GA) can solve inverse problems with a modest number of unknowns. However, we may face challenges when it comes to large-scale, highly heterogeneous fields or fields with special characteristics, such as connected preferential paths. In this thesis, firstly, we develop a new data augmentation approach, i.e., fast conditional image quilting to synthesize realizations based on limited measurements; and this approach is later used to generate channelized training images to support the inverse modeling research study. Secondly, unlike MCMC and optimization approaches that require many forward model evaluations in each iteration, we develop two neural network inverse models on full dimensions (NNI) and principal components (NNPCI) to directly explore the inverse relationships between indirect measurements such as hydraulic heads and the underlying parameter fields such as hydraulic conductivity. We successfully apply our neural network models to large-scale hydraulic tomography experiments to estimate spatially distributed hydraulic conductivity. In particular, with the help of principal component analysis (PCA), the number of neurons in the last layer of NNPCI is the same as that of retained principal components, thus further accelerating the algorithm and making the system scalable regardless of large-scale unknown field parameters. NNI also demonstrates satisfactory inverse results on full dimensions for both Gaussian and non-Gaussian fields with channelized patterns. The major computational advantage for NNI and NNPCI is that the training data can be generated by independent forward model simulations that can be done efficiently using parallel computing. Finally, to account for errors from different sources, including input errors, model structure errors, model parameters errors, etc., we incorporate Bayesian theorem to the neural network models for uncertainty analysis. The system behaves more stably and consistently on varying spatial and temporal scales. The developed approaches are successfully validated with synthetic and field cases.Ph.D

Assessment of Land Degradation Patterns in Western Kenya : Implications for Restoration and Rehabilitation

Land degradation remains a major threat to the provision of environmental services and the ability of smallholder farmers to meet the growing demand for food. Understanding patterns of land degradation is therefore a central starting point for designing any sustainable land management strategies. However, land degradation is a complex process both in time and space making its quantification difficult. There is no adequate monitoring of many of the land degradation issues both at national and local scale in Kenya. The objective of this study conducted between 2009 and 2012 was to assess the land degradation patterns in Kenya as a basis for making recommendations for sustainable land management. The correlation between vegetation and precipitation and the change in vegetation over the period 2001-2009 was assessed using 250 m resolution Moderate Resolution Imaging Spectroradiometer - Normalized Difference Vegetation Index (MODIS/NDVI) and time-series rainfall data. The assessment at national levels revealed that, irrespective of the direction of change, there was a significant correlation between vegetation (NDVI) and annual precipitation for 32% of the land area. The inter-annual change in vegetation cover, depicted by the NDVI slope, was between -0.067 and +0.068. A negative NDVI slope (indication of degradation) was observed for areas around Lake Turkana and several districts in eastern Kenya. Positive NDVI trends were observed in Wajir and Baringo, which are located in the dry land areas, showing that the vegetation cover was increasing over the years. NDVI difference between the baseline (2001-2003) and end line (2007-2009) showed an absolute change in NDVI of -0.42 to +0.48. But the relative change was between -74% for the degrading areas and +238% for the improving areas with most of the dramatic positive changes taking place in the drylands. Relative to the baseline, 21% of the land was experiencing a decline in the vegetation cover, 12% was improving, while 67% was stable. Classification of Landsat imagery for the period 1973, 1988 and 2003 showed that there were significant changes in land use land cover (LULC) in the western Kenya districts with the area under agricultural activities increasing from 28% in 1973 to 70% in 2003 while those under wooded grassland decreasing from 51% to 11% over the same period. Detailed field observations and measurements showed that over 55% of the farms sampled lacked any form of soil and water conservation technologies. Sheet erosion was the most dominant form of soil loss observed in over 70% of the farms. There was a wide variability in soil chemical properties across the study area with values of most major properties being below the critical thresholds needed to support meaningful crop production. Notable was the high proportion (90%) of farms with slightly acidic to strongly acidic (pH Erfassung und Bewertung verschiedener Erscheinungsformen von Landdegradation in West Kenia: Konsequenzen für Restaurierungs- und Rehabilitierungsmaßnahmen Landdegradation stellt eine der größten Gefahren für die Bereitstellung von Umweltdienstleistungen dar und für die Kleinbauern hinsichtlich des wachsenden Bedarfs an Nahrungsmitteln. Die Entwicklung nachhaltiger Landnutzungsstrategien beginnt daher mit dem Erkennen und Verstehen von Landdegradationsmustern. Die komplexen Prozesse der Landdegradation über Raum und Zeit erschweren jedoch eine Quantifizierung. Bisher existiert in Kenia kein adäquates Monitoring der Landdegradation, weder auf nationaler noch auf lokaler Ebene. Das Ziel des von 2009 bis 2012 durchgeführten Studie war die Erfassung von Landdegradationsmustern in Kenia, um Empfehlungen für nachhaltige Landmanagementstrategien geben zu können. Die Korrelation zwischen Vegetation und Niederschlag und der Vegetationsveränderungen im Zeitraum 2001 bis 2009 wurde mittels einer MODIS/NDVI (Moderate Resolution Imaging Spectroradiometer (250 m-Auflösung) - Normalized Difference Vegetation Index) ermittelt. Die Untersuchungen auf nationaler Ebene ergaben, dass, unabhängig von der Richtung des Änderungsprozesses, eine signifikante Korrelation zwischen Vegetation (NDVI) und jährlicher Niederschlagsmenge für 32% der Landfläche besteht. Die Änderung der Vegetationsdecke über mehrere Jahre, dargestellt durch die NDVI-Linie, lag zwischen -0.067 und +0.068. Eine abfallende NDVI-Linie (als Indikator für Degradation) konnte für Flächen rund um Turkana See und in mehreren Distrikten Ost-Kenias beobachtet werden. Positive NDVI-Trends traten in den Trockengebieten Wajir und Baringo auf; dies deutet darauf hin, dass die Vegetationsdichte hier über die Jahre zunahm. Die Differenz des NDVI zwischen Ausgangswerten (2001-2003) und Endwerten (2007-2009) zeigte eine absolute NDVI-Veränderung von -0.42 bis +0.48. Die relative Veränderung war jedoch -74% für degradierende Flächen und +238% für Flächen mit zunehmender Vegetationsbedeckung, wobei die höchsten positiven Veränderungen in den Trockengebieten festgestellt wurden. Im Vergleich zu den Basisdaten fand auf 21% der Flächen eine Abnahme der Vegetationsbedeckung statt, 12% der Landflächen erfuhr eine Verbesserung und 67% verzeichnete keine Veränderungen. Die Klassifizierung der Landsat-Aufnahmen von 1973, 1988 und 2003 zeigte signifikante Veränderungen in der Landbedeckung bzw. Landnutzung in den Distrikten West Kenias . Der Anteil der landwirtschaftlich genutzten Fläche stieg von 28% im Jahre 1973 auf 70% in 2003 an, während der Flächenanteil der Baum- und Strauchsavanne im gleichen Zeitraum von 51% auf 11% abnahm. Detaillierte Felduntersuchungen ergaben, dass mehr als 55% der untersuchten Farmen keine Boden- oder Wasserschutzmaßnahmen durchführen. Bodenerosion stellte die Hauptursache von Bodenverlust dar und konnte bei über 70% der Farmen festgestellt werden. Die chemischen Bodeneigenschaften im Untersuchungsgebiet waren sehr variabel; viele der wichtigsten Bodeneigenschaften lagen unter den kritischen Grenzwerten, die für erfolgreichen Pflanzenbau notwendig sind. Auffällig war der hohe Anteil an Farmen (90%) mit leicht bis sehr sauren Böden (pH<5.5). In den Böden von über 55% der Farmen lag der organischer Kohlenstoffgehalt unter 2%. Potentieller Nährstoffvorrat und -aufnahme der Böden waren sehr variabel. Flächen, die als sehr fruchtbar klassifiziert wurden, hatten ein dreifach höheres Vorratspotential an Stickstoff und Phosphor im Vergleich zu Flächen mit geringer Fruchtbarkeit. Der geschätzte potenzielle Maisertrag der Böden lag zwischen 1.6 t/ha und 2.8 t/ha. Der aktuelle Ertrag lag mit weniger als 1 t/ha jedoch darunter. Insgesamt waren die Farmer der Meinung, dass die Produktivität der Landnutzung, Tierhaltung, und Forst- und Wasserressourcen gesunken sei. Durch die Kombination verschiedener Erfassungs- und Monitoringmethoden konnten verschiedene Aspekte der Landdegradation und damit wichtige Informationen für die Entwicklung nachhaltiger Landnutzungsstrategien erfasst werden. Um Bodennährstoffmangel und niedrige Bodenproduktivität positiv zu verändern, müsste ein integriertes Bodenmanagement zur Erhöhung der Bodenfruchtbarkeit umgesetzt werden

Forests for a Better Future Sustainability, Innovation and Interdisciplinarity

This book highlights the role of research in innovation and sustainability in the forest sector. The contributions included fall within the broad thematic areas of forest science and cover crucial topics such as biocontrol, forest fire risk, harvesting and logging practices, quantitative and qualitative assessments of forest products, urban forests, and wood treatments—topics that have also been addressed from an interdisciplinary perspective. The contributions also have practical applications, as they deal with the ecological and economic importance of forests and new technologies for the conservation, monitoring, and improvement of services and forest value