Recent land cover change estimates show overall decline of tropical forests at the regional and global scales caused by multiple social, cultural and economic factors. There is an overall concern on the prevailing land use practices, such as shifting cultivation and extraction of forest materials as agents of forests losses, but also new, emerging land uses are threatening tropical forests. Understanding of the long-term development and driving forces of forest changes are needed, especially at local levels where many decisions on forest policies and land uses are made.

Large-scale land conversion for agriculture in Brazilian Amazonia is occurring at persistently high rates. Basin-wide net land use and land cover changes imply substantially different situations between distinct regions and states due to different agricultural policies. This research used eight landscape metrics to quantify and investigate the spatial patterns of cattle pasture and cropland throughout the states of Pará, Mato Grosso, Rondônia, and Amazonas.

Accurately quantifying changes in soil carbon (C) stocks with land-use change is important for estimating the anthropogenic fluxes of greenhouse gases to the atmosphere and for implementing policies such as REDD (Reducing Emissions from Deforestation and Degradation) that provide financial incentives to reduce carbon dioxide fluxes from deforestation and land degradation. Despite hundreds of field studies and at least a dozen literature reviews, there is still considerable disagreement on the direction and magnitude of changes in soil C stocks with land-use change.

Chennai city the capital of Tamil Nadu is located in southeastern India. Its average population growth rate is 25% per decade, which recurrently alters the city's land-cover particularly the receding green-cover distressed the city's self-renewal capacity, in terms of groundwater recharge, pollution sequestration and microclimatic amelioration. This has been appraised by means of a GIS model. This model was developed using three sets of green-cover associated parameters, namely air quality amelioration, hydrological process regulation and microclimatic amelioration.

It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations. To better manage the natural resources, and to better understand the closely linked Earth systems, the concept of Digital Earth has been strongly promoted since US Vice President Al Gore's speech in 1998. One core element of Digital Earth is the use and integration of remote sensing data.

The Cane Resources Network for Southern Africa evaluated how bioenergy from sugarcane can support sustainable development and improve global competitiveness in the region. The assessment of six countries with good contemporary potential for expanding sugarcane cultivation described in this paper was part of their analysis. Its principal objective was to identify land where such production will not have detrimental environmental and/or socio-economic impacts.

The diversity and abundance of wild bees ensures the delivery of pollination services and the maintenance of ecosystem diversity. As previous studies carried out in Central Europe and the US have shown, bee diversity and abundance is influenced by the structure and the composition of the surrounding landscape. Comparable studies have so far not been carried out in the Mediterranean region. The present study examines the influence of Mediterranean landscape context on the diversity and abundance of wild bees. To do this, we sampled bees in 13 sites in olive groves on Lesvos Island, Greece.

Peri-urban areas are often characterized by valuable natural environments that provide essential life-support functions and ecosystem services for urban residents. Global environmental change has raised concerns over how urbanization and land use and land cover change affect ecosystem services. This study applied systems ecology theory and ecological energetic analysis to value the worth of natural environment and ecosystem services to a socioeconomic system. We begin with a general discussion of peri-urban areas and their ecosystem services.

The Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra daily snow cover product MOD10A1 was compared with in situ climate station measurements and a snow map generated from Landsat Enhanced Thematic Mapper Plus (ETM+) data. Snow-covered area (SCA) dynamics were assessed using the MODIS 8-day snow cover composite product MOD10A2 for the 2001–2005 snow seasons in northern Xinjiang, China. The results indicate that the snow-mapping agreement between MODIS daily snow maps and surface observations is high at 94.6% over the four snow seasons under clear-sky conditions.

Most localities of the critically endangered species Gagea bohemica (early star-of-Bethlehem) known in the Czech Republic were surveyed using the Braun-Blanquet approach. Based on formal definitions of the expert system for Czech non-forest vegetation, 69% of the 255 samples analysed were classified as already described pioneer plant communities on shallow soils. Samples unsorted by the expert system exhibit local or transient species composition.

Marked spatiotemporal variabilities in mosquito infection of arboviruses require adaptive strategies for determining optimal field-sampling timeframes, pool screening, and data analyses. In particular, the error distribution and aggregation patterns of adult arboviral mosquitoes can vary significantly by species, which can statistically bias analyses of spatiotemporal-sampled predictor variables generating misinterpretation of prolific habitat surveillance locations.

An important consideration when interpreting eddy-covariance (EC) flux-tower measurements is the spatial distribution of forest land surface cover and soil type within the EC flux-tower footprint. At many EC flux-tower sites, there is a range of geospatial data available with the ability to estimate the spatial distribution of forest land cover and soils. Developing methods that utilize multiple geospatial data sets will result in more thorough estimates of ecosystem C stock distributions.