This paper examines environmental disturbances related to energy development in the Orenburg region through a two-pronged approach. First, it ranks environmental performance per 25 administrative districts based on multidimensional clustering of 10 diagnostic indicators. Second, it examines land-use and land-cover (LULC) patterns in nine western administrative districts by classifying Landsat imagery and quantifying surface disturbance for the year 2001. Spatial analysis and descriptive statistics help identify the most disturbed administrative units.

The purpose of this study was to characterize the land use, vegetation structure, and diversity in the Barnowpara Sanctuary, Raipur district, Chhattisgarh, India through the use of satellite remote sensing and GIS. Land cover and vegetation were spatially analyzed by digitally classifying IRS 1D LISS III satellite data using a maximum likelihood algorithm. Later, the variations in structure and diversity in different forest types and classes were quantified by adopting quadratic sampling procedures.

Electrocution on overhead power structures negatively affects avian populations in diverse ecosystems worldwide, contributes to the endangerment of raptor populations in Europe and Africa, and is a major driver of legal action against electric utilities in North America. We investigated factors associated with avian electrocutions so poles that are likely to electrocute a bird can be identified and retrofitted prior to causing avian mortality.

The European Commission Directorate-General for the Environment (DG Environment) and the European Environmental Agency (EEA) have identified soil organic matter conservation and mitigation of soil loss by erosion as priorities for the collection of policy-relevant soil data at the European scale. In order to support European Union (EU) soil management policies, soil quality indicators are required that can be applied using harmonized data for the EU Member States.

In the last decades, due to climate changes, soil deterioration and land use/land cover (LULC) changes, land degradation (LD) has become one of the most important issues at the global, regional and local scale. In concrete terms, LD determines a reduction in the productivity of a territory and in its capacity of providing ecosystem goods and services. “Syndromes” of LD can be assessed in the past, and scenarios, conversely, can be developed for the future, as information baselines for sustainable land management strategies and interventions.

The frequency and size of wildfires within the Mojave Desert are increasing, possibly due to climate and land cover changes and associated increases in non-native invasive plant biomass, as measured by normalized difference vegetation index (NDVI). These patterns are of particular concern to resource managers in regions where native plant communities are not well adapted to fire.

Land-cover updating from remote-sensing data is an effective means of obtaining timely land-cover information. An automatic approach integrating iterative training sample selection (ITSS) and a Markov Random Field (MRF) model is proposed in this study to overcome the land-cover update problem when no previous remote-sensing data corresponding to the land-cover data are available.

In this article, we evaluate and compare results from three integrated assessment models (GCAM, IMAGE, and ReMIND/MAgPIE) regarding the drivers and impacts of bioenergy production on the global land system. The considered model frameworks employ linked energy, economy, climate and land use modules. By the help of these linkages the direct competition of bioenergy with other energy technology options for greenhouse gas (GHG) mitigation, based on economic costs and GHG emissions from bioenergy production, has been taken into account.

Landscape dynamics result from forestry and farming practices, both of which are expected to have diverse impacts on ecosystem services (ES). In this study, we investigated this general statement for regulating and supporting services via an assessment of ecosystem functions: climate regulation via carbon sequestration in soil and plant biomass, water cycle and soil erosion regulation via water infiltration in soil, and support for primary production via soil chemical quality and water storage.

Rapidly accelerating climate change in the Himalaya is projected to have major implications for montane species, ecosystems, and mountain farming and pastoral systems. A geospatial modeling approach based on a global environmental stratification is used to explore potential impacts of projected climate change on the spatial distribution of bioclimatic strata and ecoregions within the transboundary Kailash Sacred Landscape (KSL) of China, India and Nepal.

Accurate built-up information is imperative for loss estimation and disaster management after the occurrence of catastrophic events such as earthquake, tornado, tsunami and flood. These catastrophic events leave behind a trail of mass destruction with property and human losses amounting to millions. Once a natural disaster hits a region, built-up information is required within a short span of time for disaster management. Nowadays, earth observation satellite imagery serves as a promising source to extract the land use / land cover classes.

In this work, we used the Regional Hydro‐Ecological Simulation System (RHESSys) model to examine runoff sensitivity to land cover changes in a mountain environment. Two independent experiments were evaluated where we conducted simulations with multiple vegetation cover changes that include conversion to grass, no vegetation cover and deciduous/coniferous cover scenarios. The model experiments were performed at two hillslopes within the Weber River near Oakley, Utah watershed (USGS gauge # 10128500).