Understanding how landscape, host, and pathogen traits contribute to disease exposure requires systematic evaluations of pathogens within and among host species and geographic regions. The relative importance of these attributes is critical for management of wildlife and mitigating domestic animal and human disease, particularly given rapid ecological changes, such as urbanization.

Complex mountainous environments such as Himalayas are highly susceptibility to natural hazards particular those that are triggered by the action of water such as floods, soil erosion, mass movements and siltation of the hydro-electric power dams. Among all the natural hazards, soil erosion is the most implicit and the devastating hazard affecting the life and property of the millions of people living in these regions.

Multiple classifier systems (MCS) are attracting increasing interest in the field of pattern recognition and machine learning. Recently, MCS are also being introduced in the remote sensing field where the importance of classifier diversity for image classification problems has not been examined.

When attempting to address the environmental problems of a catchment, it is important to consider changes in a long-term environmental context. However, the long-term data on the state of the environment that are required for such an examination are rarely documented. Such data collection typically requires several years of investigation and observation. In addition, as there may be a significant time lag between the occurrence of a phenomenon and its cause, subsequent environmental investigations of changing animal and plant states scaling up to 5 years may be inadequate.

Erosion is one of the most important natural hazard phenomena in the world, and it poses a significant threat to Turkey in terms of land degredation and desertification. To cope with this problem, we must determine which areas are erosion-prone. Many studies have been carried out and different models and methods have been used to this end. In this study, we used a logistic regression to prepare an erosion susceptibility map for the Ayvalık region in Balıkesir (NW Turkey).

Ecology and Quaternary palaeoecology have largely developed as parallel disciplines. Although both pursue related questions, information exchange is often hampered by particularities of the palaeoecological data and a communication gap has been perceived between the disciplines. Based on selected topics and developments mainly in Quaternary palaeoecology, we show that both disciplines have converged somewhat during recent years, while we still see untapped potential for closer interactions.

Remote sensing data is routinely used in ecology to investigate the relationship between landscape pattern as characterised by land use and land cover maps, and ecological processes. Multiple factors related to the representation of geographic phenomenon have been shown to affect characterisation of landscape pattern resulting in spatial uncertainty. This study investigated the effect of the interaction between landscape spatial pattern and geospatial processing methods statistically; unlike most papers which consider the effect of each factor in isolation only.

Trypanosoma cruzi, the etiologic agent of Chagas' disease, is enzootic in animal populations of the southeastern United States. In the United States, T. cruzi prevalence has been reported for over 20 different wildlife species, and 7 autochthonous human cases have been documented since 1955. Previous canine (Canis familiaris) serosurveys have been limited either by small sample size or confined geographic reporting areas.

The distributed hydrology soil vegetation model (DHSVM) is applied in the 107 km² Nam Mae Rim watershed (NMRW) in northern Thailand. Simulations using land cover scenarios for 1989 and 2002, extreme deforestation, and forest, each run with and without roads, show that roads have very small effects on the mean water fluxes, but significantly increase peak flows for all land cover scenarios. The magnitude of the road effect on peak flow depends on the land cover context in which the roads are placed.

Remotely-sensed images and Geographical Information Systems (GIS) data were integrated into the SLEUTH cellular automata (CA) model to analyze land-use/land-cover dynamics in Chiang Mai city and its surrounds. The land-use and land-cover statistics, obtained from GIS data base and satellite images from 1952, 1977, 1989 and 2000 revealed rapid increased in urbanization during these periods. To understand the underlying causes of land-use and land-cover dynamics, remote sensing, GIS and modeling techniques were applied.

Outdoor water use is a key component in arid city water systems for achieving sustainable water use and ensuring water security. Using evapotranspiration (ET) calculations as a proxy for outdoor water consumption, the objectives of this research are to quantify outdoor water consumption of different land use and land cover types, and compare the spatio-temporal variation in water consumption between drought and wet years.

Central Asia is one of the most vulnerable regions on the planet earth to global climate change, depending on very fragile natural resources. The Soviet legacy has left the five countries (Kazakhstan, Tajikistan, Kyrgyzstan, Turkmenistan, and Uzbekistan) with a highly integrated system but they are facing great challenges with tensions that hinder regional coordination of food and water resources.