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.

This research was carried out with the aim of presenting an environmental management plan for steel production complex (SPC) in central Iran. Following precise identification of the plant activities as well as the study area, possible sources of environmental pollution and adverse impacts on the air quality, water, soil, biological environment, socioeconomic and cultural environment, and health and safety of the employees were determined considering the work processes of the steel complex. Afterwards, noise, wastewater, and air pollution sources were measured.

Scale is a fundamental concept in landscape ecology and considerable attention has been given to the scale-dependent relationships of landscape metrics. Many metrics have been found to exhibit very consistent scaling relationships as map resolution (i.e., pixel or grain size) is increased. However, these scaling relationships tend to break down when attempting to ‘downscale’ them, and the scaling function is often unable to accurately predict metric values for finer resolutions than the original data. The reasons for this breakdown are not well understood.

Regional estimates of soil carbon pool have been made using various approaches that combine soil maps with sample databases. The point soil organic carbon (SOC) densities are spatialized employing approaches like regression, spatial interpolation, polygon based summation, etc. The present work investigates a data mining based spatial imputation for spatial assessment of soil organic carbon density. The study area covers Andhra Pradesh and Karnataka states of India.

Increasing interest in wetlands for environmental management requires an understanding of the location, spatial extent, and configuration of the resource. The National Wetlands Inventory is the most commonly used data source for this information. However, its accuracy is limited in some contexts, such as agricultural and forested wetlands. An large number of studies have mapped wetlands worldwide from the perspective of land use and land cover change. However, information on the actual wetland planting areas annually is limited, which greatly impacts ongoing research.

The UAE has witnessed rapid urban development and economic growth in recent years. With its ambitious vision to become one of the advanced nations by 2021, planners and policy-makers need to know the most likely direction of future urban development. In this study, remotely sensed imagery coupled with cellular automata models were used to predict land cover in Al Ain, the second largest city in the Emirate of Abu Dhabi. Markov and cellular automata models were used for 1992 and 2006 to predict land cover in 2012.

This paper proposes an automatic framework for land cover classification. In majority of published work by various researchers so far, most of the methods need manually mark the label of land cover types. In the proposed framework, all the information, like land cover types and their features, is defined as prior knowledge achieved from land use maps, topographic data, texture data, vegetation’s growth cycle and field data.

The dynamics, degradation, and conservation of forest ecosystems are matters of prime concerns worldwide at the present. Proper planning and management of a forest area are essentially needed to protect it from the grasp of burgeoning pressure of urban-industrial sprawl. Establishment of eco-sensitive zones (ESZs), which act as buffer areas around the core forests, is one of the key approaches towards achieving this goal.

Lake Chad in Africa experienced severe droughts in the 1970s and 1980s and overexploitations of water resulting in a decline of water level in the Lake and surrounding rivers. Such droughts and overexploitation of water caused a significant change of land use and water management practices over the last 50� years. Understanding the change of land use and land cover is, therefore, crucial to understand disturbance of the water cycle around the Lake.

Inland waters transport large amounts of dissolved organic matter (DOM) from terrestrial environments to the oceans, but DOM also reacts en route, with substantial water column losses by mineralization and sedimentation. For DOM transformations along the aquatic continuum, lakes play an important role as they retain waters in the landscape allowing for more time to alter DOM. We know DOM losses are significant at the global scale, yet little is known about how the reactivity of DOM varies across landscapes and climates. DOM reactivity is inherently linked to its chemical composition.

Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland.

Landscape homogenisation represents one of the gravest threats to the biodiversity of intensively farmed landscapes. In such landscapes, many species persist within remnants of (semi)natural habitats, such as in the steppe grasslands of Southern Moravia, SE Czech Republic. We investigated how the butterfly fauna of insular grassland reserves is affected by the heterogeneity of the surrounding farmland.